Sérgio Pires

Afonso Independent

A More Precise Approach to Fiscal Consolidation and Sustainability

This paper argues that austerity is not a good solution for fiscal consolidation and sustainability. Therefore, it is imperative to find a new approach. This paper presents a mechanism to improve both tax compliance and fiscal sustainability.

Introduction

This paper argues that austerity does not work as intended. For instance, in the Eurozone countries undergoing austerity measures, the debttoGDP ratios keep rising and will create a larger problem in the future (Grauwe and Ji, 2013a) . It is therefore of the utmost importance to find alternatives that do work for fiscal consolidation and sustainability. This paper presents one such alternative. In the next part of the paper, I will provide background information to explain why austerity fails. I will then introduce a new fiscal mechanism.

Literature Review

Austerity is selfdefeating, particularly during a recession. Cutting government spending and raising taxes causes a drop in the GDP because households have less disposable income, which leads to lower tax revenue consequently, the deficit fails to improve [START_REF] Krugman | Myths of austerity[END_REF].

What can then happen is that growth can be less than forecasted [START_REF] Blanchard | Growth forecast errors and fiscal multipliers[END_REF].

Austerity leads to more unemployment, which causes more spending and makes controlling the deficit more difficult. It can even lead to the budget deficit increasing. Austerity does not work at all during a recession, particularly when interconnected countries apply it simultaneously [START_REF] Holland | Selfdefeating austerity?[END_REF]Portes, 2012; Wachtmeister andÖdeen, 2015). As a result of all the factors described above, the debttoGDP ratios have risen more in the Eurozone countries that have applied harsher austerity measures (Grauwe and Ji, 2013b).

A highly relevant consequence of austerity is that it decreases tax compliance ( Pappadà and Zylberberg, 2014; Carvalho and Pacheco, 2014; Schneider and Kearney, 2013). One of the reasons is that higher taxes lead people towards noncompliance, while another is that economic hardships force people to work in informal markets to make a living. This makes it even more challenging to achieve fiscal consolidation and sustainability.

Governments cannot sustain a large deficit indefinitely, but austerity measures do not work and the debt continues to grow; therefore, a new solution is necessary.

A Better Approach

In this framework, government wages and transfers would be linked to tax revenue, and the former would rise if the latter also rose, providing an incentive to increase tax compliance.

However, not all of the new revenue would go to wages and transfers. The real goal of this framework is to have some of it go towards debt amortization. The amount for the wages would have to be sufficiently high to increase tax compliance, but low enough to provide more money for debt amortization, Under the current fiscal framework, government employees and transfer recipients have the same income regardless of their tax compliance. By linking tax compliance, government wages and transfers closely, this type of budget would help to reduce tax evasion. Citizens could see very clearly that, if they do not pay taxes, their wages and transfers will be lower. One of the problems of austerity is that higher taxes mean a higher degree of tax evasion, making it more difficult to balance the budget. This type of mechanism addresses this factor.

Wages and transfers would have a fixed component, which would be the same value as at present, and a variable component linked to an increase in tax revenue. This variable part would start at zero and could not become lower.

If tax revenue did indeed increase, the money would be stored in a bank account to be distributed as soon as possible to ensure that people received their rewards quickly in order to make compliance more attractive.

Tax increases would be measured by having a number of thresholds, whereafter the variable component of the wages and transfers would increase by a preset amount.

These measures would have the effect of reducing the size of the informal economy, which is €2.15 trillion in Europe ( Schneider and Kearney, 2013) , and would be highly relevant for fiscal sustainability. Motivating formal markets could create a virtuous cycle in which more people leave the grey economy and join the official one. One of the things that can result from this is social norms changing and making it less socially appealing to work in the informal market.

Conclusion

Connecting the value of government wages and social transfers to tax revenue would motivate tax compliance. The main goal of this strategy is to have a significant portion of the new tax revenue going to debt amortization, thereby contributing significantly to fiscal consolidation and sustainability.


Bin Tian
email: bin.tian@isima.fr

Kun Mean Hou

Jianjin Li
email: jianjin.li@isima.fr

TrAD: Traffic Adaptive Data Dissemination Protocol for Both Urban and Highway VANETs

Keywords: Vehicular Ad hoc Networks (VANETs), traffic adapting, data dissemination protocol, broadcast suppression technique, Store-Carry-Forward mechanism I

Vehicular Ad hoc Networks (VANETs) aim to improve transportation activities that include traffic safety, transport efficiency and even infotainment on the wheels, in which a great number of traffic event-driven messages are needed to disseminate in a region of interest timely. However, due to the nature of VANETs, highly dynamic mobility and frequent disconnection, data dissemination faces great challenges. Inter-Vehicle Communication (IVC) protocols are the key technology to mitigate this issue. Therefore, we propose an infrastructure-less Traffic Adaptive data Dissemination (TrAD) protocol that considers road traffic and network traffic status for both highway and urban scenarios. TrAD is flexible to fit the irregular road topology and owns double broadcast suppression techniques. Three state-of-the-art IVC protocols have been compared with TrAD by means of realistic simulations. The performance of all protocols is quantitatively evaluated with different real city maps and traffic routes. Finally, TrAD gets an outstanding overall performance in terms of several metrics, even though under the worse condition of GPS drift.

INTRODUCTION

Vehicular Ad hoc Networks (VANETs) are believed to be the crucial technology for Intelligent Transportation Systems (ITS) and Traffic Information Systems (TIS), which can be used to support the development of various applications related to traffic safety, transport efficiency and even entertainment on the wheels [START_REF] Karagiannis | Vehicular Networking: A Survey and Tutorial on Requirements, Architectures, Challenges, Standards and Solutions[END_REF]. However, the main characteristics of VANETs are spatially and temporally localized, highly dynamic and data-intensive, which causes many difficulties to the design of IVC protocols that is the key technology of VANETs [START_REF] Bai | Exploiting the wisdom of the crowd: localized, distributed information-centric VANETs [Topics in Automotive Networking[END_REF]. To cope with these issues, the broadcast communication paradigm is usually used to disseminate data messages [START_REF] Tonguz | DV-CAST: A distributed vehicular broadcast protocol for vehicular ad hoc networks[END_REF]- [START_REF] Villas | DRIVE: An efficient and robust data dissemination protocol for highway and urban vehicular ad hoc networks[END_REF], since broadcasting is flexible to transmit particular data to a number of uninformed vehicles in a region of interest (ROI). Moreover, several broadcast protocols have been proposed to prove the possibility and the applicability of disseminating data in both urban and highway scenarios [5], [START_REF] Villas | DRIVE: An efficient and robust data dissemination protocol for highway and urban vehicular ad hoc networks[END_REF].

However, there are many challenges to design broadcast protocols. The road traffic environments in urban and highway scenarios are totally different. In urban scenario, there are various road topologies and many buildings beside roads. Several dense networks would be composed around some traffic hubs during rush hour. Due to the existence of vehicular mobility and building obstacles, these dense vehicular networks are partitioned, so that the disconnected network problem is ubiquitous. Therefore, how to suppress the broadcast storm in dense networks and how to fill the gap between these disconnected networks are the crucial challenges. Although the road topology is relatively simple in highway scenario, the high vehicular density and the high speed are also issues to the design of protocols. In addition, IEEE 802.11p standard does not establish a Basic Service Set (BSS) and its Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) mechanism has not the acknowledgments process. These amendments indeed bring a fast connection process, but leads to an unreliable connectivity of broadcasting.

In this paper, we attempt to address these issues by proposing an infrastructure-less Traffic Adaptive data Dissemination (TrAD) protocol to work seamlessly in both urban and highway scenarios. The TrAD protocol includes two components: one is a broadcast suppression technique that uses a time slot scheme to constrain the broadcast storm problem and improves the reliability of transmission. The other is a store-carry-forward mechanism that not only selects appropriate vehicles to fill the connectivity gap between different disconnected networks, but also further suppresses the redundant transmissions. Furthermore, a comprehensive performance evaluation is performed by means of realistic simulations. We compare TrAD with three state-of-the-art IVC protocols in both urban and highway scenarios.

The remainder of this paper is organized as follows. In the next section, we summarize the progress of related literature on this subject, and then present the challenges that need to be resolved. That motivates us to propose the TrAD protocol in section Ⅲ, where every mechanism of TrAD is elaborated. In section Ⅳ, we evaluate the performance of TrAD by means of realistic simulations. Finally, we conclude this paper and present the future work.

II. RELATED WORK

Most of data dissemination protocols dedicated their contributions to resolve two main problems. One is the broadcast storm problem in the well-connected network. The broadcast suppression technique is used to mitigate this problem. The other is the disconnected network or network partition problem. For this issue, various store-carry-forward mechanisms have been designed.

A. Broadcast Suppression Technique

Tseng et al. [START_REF] Tseng | The Broadcast Storm Problem in a Mobile Ad Hoc Network[END_REF] discovered the broadcast storm problem and proposed three pioneer schemes to alleviate it. Nevertheless, these schemes are specified for Mobile Ad hoc Networks (MANETs). Wisitpongphan et al. [START_REF] Wisitpongphan | Broadcast storm mitigation techniques in vehicular ad hoc networks[END_REF] proposed some interesting techniques for highway VANETs that include weighted p-persistence, slotted 1-persistence, and slotted p-persistence. Schwartz et al. [START_REF] Schwartz | A directional data dissemination protocol for vehicular environments[END_REF] improved the slotted 1-persistence for their simple and robust dissemination (SRD) protocol. The optimized slotted 1-persistence defines different priorities in two directions of highway. However, all above-mentioned efforts focused on the one-dimensional highway scenario.

In order to handle broadcast storm in two-dimensional urban scenario, eMDR uses real roadmap and GPS information to identify the location of vehicles, so that it can operate the data dissemination [START_REF] Fogue | Evaluating the impact of a novel message dissemination scheme for vehicular networks using real maps[END_REF]. Viriyasitavat et al. [START_REF] Viriyasitavat | UV-CAST: an urban vehicular broadcast protocol[END_REF] proposed the UV-CAST protocol where an illustrative equation is presented to calculate a wait time to rebroadcast in terms of position and distance. It assigns a shorter rebroadcast delay to the vehicle located at an intersection, which intends to disseminate data message in more directions. However, Fogue et al. [START_REF] Fogue | Securing Warning Message Dissemination in VANETs Using Cooperative Neighbor Position Verification[END_REF] pointed out that eMDR and UV-CAST should not blindly trust the GPS positioning that is usually not accurate. Thus, the impact of GPS drift on the performance of protocols should be evaluated.

DRIVE and AMD protocols provided solutions to classify neighbors into different quadrants or sectors, which can support to disseminate data in both urban and highway scenarios [5], [START_REF] Villas | DRIVE: An efficient and robust data dissemination protocol for highway and urban vehicular ad hoc networks[END_REF]. DRIVE divides the communication area into four quadrants and selects a sub-area in each quadrant called sweet spot. The vehicles in sweet spot get a shorter rebroadcast delay. AMD adaptively separates the vehicle's communication area into 2 or 4 equal sectors according to the road topology and the distribution of neighbors. However, the road topology in the real world is not always regular like Manhattan-Grid style. The approaches of these two protocols are not flexible enough to fit irregular road topologies.

B. Store-Carry-Forward Mechanism,

For disconnected network problem, most of the literature mainly focused on the selection of SCF-agent vehicles that can store and carry the data message until they meet new opportunities to forward. UV-CAST attempts to select the vehicles located at the boundary of connected network to be SCF-agents by a gift-wrapping algorithm. Since this algorithm is a distributed version, the set of SCF-agent vehicles is always a superset of all boundary vehicles [START_REF] Viriyasitavat | UV-CAST: an urban vehicular broadcast protocol[END_REF]. Thus, UV-CAST would trigger more redundant transmissions. DRIVE does not resort to beaconing, where it only sets a timer for the vehicles outside the ROI to schedule the rebroadcast [START_REF] Villas | DRIVE: An efficient and robust data dissemination protocol for highway and urban vehicular ad hoc networks[END_REF]. It is obvious that most opportunities of transmissions would be missed. AMD does not suffer this issue, it uses beaconing that can trigger a new broadcast when a vehicle make transition from a role of tail to non-tail in one of its directional sectors [5]. Nevertheless, the directional sector classification of AMD can not accurately recognize the irregular road topology, which would lead to a failure to detect some transitions so as to miss some opportunities to forward data messages.

III. TRAFFIC ADAPTIVE DATA DISSEMINATION

A. Concepts

Several concepts of TrAD are defined, which will be used throughout the paper. The illustration of TrAD protocol is shown in Fig. 1.

• Directional Cluster : It is a group of vehicles in the neighborhood of a sender S, which are in a similar direction with respect to the sender S. • Coordinator: This concept has been proposed in GPCR protocol [START_REF] Lochert | Geographic Routing in City Scenarios[END_REF]. The coordinator is the vehicle that is located at an intersection. For instance, the sender S in Fig. 1. (the red vehicle) is a coordinator. • Breaker: In a well-connected network, the breaker is not only the furthest vehicle but also the one which moves towards outside of the network. For instance, the green vehicle V0 in Fig. 1 is a breaker.

B. The Protocol

In this section, we propose the principle of operation for the TrAD protocol, which consists of two main components: the broadcast suppression technique for well-connected network and the store-carry-forward mechanism for disconnected network (Fig. 2). TrAD requires beaconing to maintain the up-to-date status in a one-hop neighborhood. The structure of beacon is < Beacon ID, Sender ID, Global GPS Position, Driving Direction, Number of Neighbors, Channel Busy Ratio, Message List >. The message list entry contributes to recognize the uninformed vehicle by comparing neighbor's message list with local message list.

1) Broadcast Suppression Technique

The protocol makes a centralized decision in the sender to control the rebroadcast order of neighbors, since the centralized decision can resolve the hidden terminal problem in the multi-directional dissemination.

a) Vector-angle-based cluster classification mechanism

This classification mechanism only resorts to position information without road map, so that it can fit more complex road topologies. The algorithm uses vector angle to identify whether the vehicles belong to a directional cluster.

The operation is described as follows (Fig. 1): In the wellconnected network, the sender S extracts the first neighbor 1 from its neighbor list and classifies 1 into the directional cluster . Then, the algorithm calculates the vector from S to 1 ( 1 ). After that, S extracts next neighbor 2 and forms another vector from S to 2. The angle α between and can be obtained from their dot (or scalar) product (α 0, π ). It is compared with a threshold angle α α 10°). If the angle α is less than α, the neighbor 2 is classified into the directional cluster . If not, neighbor 2 still remains in the neighbor list for next step. In Fig. 1,α α , so 2 . This process continues until all neighbors have been checked. Therefore, we can identify a group of vehicles that are located in similar directions as 1. The members of are saved in a cluster buffer and eliminated from next classification step. The classification process continues to be performed until all neighbors have been classified into respective directional clusters.

The classification result of TrAD in Fig. 1 is

C 1 ={N1, N2}; C 2 ={N3, N4}; C 3 ={N5, N6}; C 4 ={N7
#N .#N , 1 (1)
is the metric of the distance between a sender S and a neighbor (Eq.2). Dist is the distance between a sender S and a neighbor.

. is the maximum communication range of the wireless access medium.

min D .R R , 1 (2)
The metric CBR (Eq.3) describes the channel usage of local node in a time interval (1s). The time usage of busy channel is got from PHY layer of IEEE 802.11p. Thereby, we propose an equation (Eq.4) to assign a higher transmission utility to the neighbor that owns more neighbors , further distance and smaller .

T T I (3)
U TX 1 • 1 • 2 (4)
According to Eq.4, we get the 1,8 of every neighbor. First, we sort the neighbors in every directional cluster in terms of their in decreasing order. Then, a round-robin fashion is used to define the final order of neighbors in priority list, where the vehicle with the highest in the directional cluster C 1 is put in the first position, followed by the vehicle with highest in directional cluster C 2 , and so forth [5]. This fashion guarantees the fairness for each directional cluster.

The sender S inserts the priority list into the header of data message before transmission. The format of header is < Data ID, Originator ID, Sender ID, Originator Position, Sender Position, Priority List >. When the recipients receive the data message from S, they extract the priority list and find their order 0, 1 , n is the total number of neighbors. TrAD allocates one slot time for each vehicle. The transmission delay is computed by using Eq.5.

Delay TX st • R (5)
2) Store-Carry-Forward (SCF) Mechanism SCF mechanism includes two components: one is responsible for selecting appropriate vehicles to be SCFagent. The other is a special operation to further suppress the redundant broadcasts.

a) The Selection of SCF-agent Here, we introduce the techniques to identify the coordinator and breaker. A vehicle that satisfies any one condition of them is selected to be a SCF-agent.

• Coordinator: Since an intersection position list is preloaded into the system, every vehicle can check the distance between itself and all intersections when the vehicle receives a beacon message. If any distance is less than 20m, the vehicle is believed to be a coordinator.

• Breaker: When the vehicle receives a data message, the protocol will check and eliminate the possibility of coordinator role. After that, the vehicle checks whether its driving direction is the same as data forwarding direction. If so, the vehicle will search whether there is a further neighbor driving in the data forwarding direction. If not, the vehicle is defined as a breaker. It is worth mentioning that this procedure will iterate until the boundary of well-connected network.

The coordinator is specified for the two-dimensional topology of urban scenario. The heading of coordinator after passing an intersection is various and can not be predicted. Therefore, the coordinator is beneficial to discover more uninformed vehicles. The breaker is defined for both urban and highway scenarios. It can carry the data message and drive towards outside of well-connected network.

b) SCF-agent Constrained Rebroadcast Technique

This technique aims to trigger the rebroadcast of SCFagent and constrain it simultaneously, if multiple SCF-agents receive a same request by beaconing. Thus, we design a distributed time slot scheme to calculate different broadcast delay for every SCF-agent. If a SCF-agent receives a redundant data message (echo) that is the same as the one it is scheduling, the SCF-agent will cancel the schedule and switch to an idle state.

Normally, the uninformed vehicles drive toward the SCF-agents from outside of the connected network where the SCF-agents get the data message. Therefore, we design the Eq.6 to calculate a utility of a SCF-agent ( 1,4 ), which assign a higher to the SCF-agent owns closer distance to the uninformed vehicle and smaller channel busy ratio .

2 • 2 (6)
A broadcast delay for a SCF-agent is calculated by Eq.7. This equation assigns a shorter delay to the SCF-agent with a higher . Notice that a new incoming uninformed vehicle normally does not only require one data message. It is possible to require several data messages once. If the protocol does not control this behavior properly, a burst of transmissions will be incurred. Therefore, we propose the Eq.7 to calculate the :

Delay SCF st • BurstCount 1 U SCF (7)
We give a basic slot time between each required data message. The is the transmission order of data messages. For the same data message, the • 1 U SCF part lets a SCF-agent with a higher utility transmit first. This part also works as an additional delay, which can mitigate the time slot boundary synchronization problem.

For instance, in Fig. 1, vehicles V7 and V8 are the SCFagents and carry new data messages. They received the request from the vehicles around them. Since the utility of vehicle V8 (0.8) is larger than the one of V7 (0.5), vehicle V8 is triggered to broadcast data messages first. V7 hears the redundant data and cancels the schedule of the same data message. Thus, the redundant transmissions are suppressed.

IV. PERFORMANCE EVALUATION

The performance of TrAD protocol is evaluated by means of realistic simulations in both urban and highway scenarios. A scenario includes a map and a series of traffic routes. The map is composed of road topology and building obstacles, and the traffic route specifies the planned roundtrip route for every vehicle cluster. Several traffic routes comprise the road traffic network on the map. We set the departure of vehicles following an exponential distribution. The programs are implemented to simulate three state-of-theart IVC protocols in order to compare with TrAD protocol, namely DV-CAST for highway scenario [START_REF] Tonguz | DV-CAST: A distributed vehicular broadcast protocol for vehicular ad hoc networks[END_REF], UV-CAST for urban scenario [START_REF] Viriyasitavat | UV-CAST: an urban vehicular broadcast protocol[END_REF] and AMD for both urban and highway scenarios [5].

We use the Veins 3.0 framework 1 that is based on two simulators, OMNeT++ 4.4.1, an event based network simulator 2 , and SUMO 0.23.0, a road traffic simulator 3 . IEEE 802.11p standard has been used to be the MAC and PHY layer for DSRC/WAVE. The data rate is set to 6 Mbit/s that is the default data rate of broadcasting in IEEE 802.11p. The transmission power is set to 300 mW. The Friis Free Space Path Loss (FSPL) propagation model is used, where the exponent α is assigned to 3.0, as it is in the range [2.7, 5.0] that is estimated for outdoor shadowed urban environment [START_REF] Rappaport | Wireless Communications: Principles and Practice[END_REF]. The radio range reaches approximately 366m according to the setting of propagation model. The Bit Error Rate (BER) model provided in Veins is proposed by [START_REF] Sjöberg | Measuring and using the RSSI of IEEE 802.11p[END_REF]. The shadowing obstacle model treats the buildings (pink polygon in Fig. 3) as obstacles [START_REF] Sommer | A computationally inexpensive empirical model of IEEE 802.11p radio shadowing in urban environments[END_REF]. For all simulations, the source node broadcasts the data message every 2s. The beacon message of TrAD is sent every 1s and its lifetime is 1.5s. The size of data message is 2312 bytes. The size of beacon message is 378 bytes, in which the entry number of message list is 40. The vehicle density is from 40 v/km 2 to 160 v/km 2 , the interval is 20 v/km 2 . The setting of simulation system is shown in TableⅠ. We created one highway scenario and two urban scenarios. Geographic data were retrieved from the OpenStreetMap 4 (OSM) database. The traffic rule and traffic light were set according to the real world ones. For urban scenario, two maps are used: one is a fragment of Manhattan borough of New York City, USA (Fig. 3a), the other is the downtown area of Clermont-Ferrand, France (Fig. 3b). The size of both maps is 1 x 1 km 2 . The data message is generated by a fixed node (source) located at an intersection in the center of each map and collected by all mobile vehicles in the ROI. The highway map is selected from a section of A711 highway near Clermont-Ferrand airport (Aulnat), France. It consists in 2km straight long and two lanes in each direction. The data message is created by one fixed node (source) at the west end of the highway and gathered by another fixed node (receiver) at the east end.

A. Maps

The statistics of maps (TableⅡ) shows that the map of Clermont-Ferrand has more lanes, junctions and shorter street lengths than Manhattan, which means that the traffic environment is more complex in Clermont-Ferrand than in Manhattan. Similar traffic routes are created for both maps, which lets vehicles be distributed uniformly in the scenario. According to the Fig. 4 and Fig. 5, TrAD uses moderate number of transmissions (overhead) to achieve better or similar packet delivery ratio (PDR) and delay compared with UV-CAST. Although UV-CAST gets similar PDR or delay with TrAD in a few experiments, it consumes too much overhead. In contrast, AMD well controls its overhead. However, it fails to reach qualified PDR and delay compared with other two protocols. Therefore, TrAD has an outstanding performance cost ratio.

Specifically, the complex map of Clermont-Ferrand constrains the transmissions of TrAD. In consequence, the PDR of TrAD in Clermont-Ferrand fails to achieve 90% when the vehicular density is less than 80 v/km 2 . By contrast, the PDR of TrAD in Manhattan is more than 90% at all the vehicular densities. But when the vehicular density is more than 80 v/km 2 (include 80 v/km 2 ), the PDR of TrAD in Clermont-Ferrand exceeds 90% and the usage of overhead is at roughly the same level as Manhattan. That means TrAD is capable to adapt complex road topology.

B. Traffic Routes

For urban scenarios, most of previous works used different maps to evaluate the performance of protocols. However, we realize that different traffic routes on a same map could also significantly influence the performance of protocols. Therefore, we designed two different traffic routes based on the Manhattan map (Fig. 7). Traffic route 1 (Fig. 7a) is uniformly distributed that is the same traffic route of Manhattan scenario in Fig. 3a. It lets vehicles drive between upper and lower area of the map, so that they can cross the middle line where the source node is located. Thus, the vehicles get more chances to connect to the source node or other vehicles that carry new data. In the opposite, traffic route 2 (Fig. 7b) is non-uniformly distributed. It constrains vehicles inside the upper or the lower area of the map. Consequently, only a few vehicles pass by and connect to the source node. Therefore, traffic route 2 could create more disconnected networks than traffic route 1.

As shown in Fig. 5 and Fig. 6, TrAD still maintains a relatively higher performance cost ratio compared with other two protocols. The gap of number of transmissions between TrAD and AMD is enlarged in the scenario with traffic route 2 compared with the case of traffic route 1. The PDR of TrAD in the scenario with traffic route 2 first reaches 90% and catches up with the same level as traffic route 1 case just after 60 v/km 2 vehicular density. This result illumines us that TrAD can mitigate the influence of disconnected network. In summary, the complex map and non-uniform traffic route indeed impact the performance of data transmission protocols. However, experiment data shows that TrAD costs moderate transmissions to achieve a relatively high PDR and low delay. The reason lies in two factors: one is that TrAD uses the flexible cluster classification to identify the complex road topology, and the traffic adaptive sorting algorithm to select appropriate vehicles to rebroadcast the data message. The other is that the SCF mechanism of TrAD not only discovers more potential uninformed vehicles, but also suppresses the redundant transmissions of SCF-agents. In contrast, UV-CAST triggers more transmission by using its gift-wrapping algorithm and there is no suppression technique in SCF mechanism. The sector classification of AMD is not flexible for the complex map and its SCF mechanism fails to discover more potential uninformed vehicles.

C. Network Density 1) Urban Scenario

The simulation results of different maps and traffic routes in urban scenarios with the increasing of network density have been elaborated. We realize that the network density, map and traffic route are three main impact factors to the performance of IVC protocol. Each of them can influence the final result. Normally, a lower network density leads to more disconnected networks, which lets protocols get relatively low PDR, high delay and high number of transmissions (overhead). In contract, a higher network density let protocols achieve the opposite results. However, the geographic and traffic environment could also significantly affect the performance of protocols. Fig. 8a shows that TrAD is more scalable to maintain high and stable data coverage. Furthermore, the delay of TrAD is excellent at most experiments and decreases rapidly with the increasing of traffic flow (Fig. 8.c). In Fig. 8b, TrAD gains the advantage over AMD in sparse networks. While AMD consumes fewer transmissions in dense networks, which conforms with the result of experiments in [START_REF] Tseng | The Broadcast Storm Problem in a Mobile Ad Hoc Network[END_REF]. However, AMD fails to touch the level of PDR and delay of TrAD. For DV-CAST, its PDR and delay are unstable and worse than others.

2) Highway Scenario

SCF mechanism of TrAD and AMD mainly influences their performance in highway scenario. The breaker of TrAD always carries the data and drives towards the receiver. However, AMD could fail to detect opportunities to transmit, so that a part of PDR is lost and the delay is increased. The unreliability of DV-CAST lies in its complex operations that resort to several connectivity flags and timers.

D. GPS Drift

The proposed protocol is partly based on positioning. However, the GPS system is usually not accurate. Especially in urban scenario, the high-raise buildings could obstruct the signal. Therefore, we investigated the GPS drift in terms of data dissemination speed that represents the percentage of vehicles that are covered by data over time. Five error deviations (0m, 25m, 50m, 75m and 100m) have been injected into the mobility module of simulation, a moderate vehicle density 100 v/km 2 is used in the complex map (Clermont-Ferrand). The source node only sends one data message and the data coverage is recorded over time.

The tolerance of TrAD to GPS drift is evaluated (Fig. 9). We can observe that the simulation without GPS drift gets the best result. The 25m, 50m and 75m error deviations lightly impact the performance of TrAD, but the data dissemination speed of these deviations still keep close and achieve a relatively middle level result. The result of 100m deviation is worse than others, which can be predicted. But the data coverage of all the deviations reaches 100% at last. TrAD is compared with AMD and UV-CAST in 50m error deviation (Fig. 10). TrAD keeps a relatively good upward trend than other two protocols and finally reaches 100% data coverage. However, GPS drift constrains the performance of UV-CAST and lets its dissemination speed slower than AMD at most of time. Finally, UV-CAST and AMD just achieve the data coverage around 80%. These results verify that TrAD is more robust to the impact of GPS drift.

V. CONCLUSION AND FUTURE WORK

We have proposed the TrAD protocol to improve the performance of data dissemination for both highway and urban scenarios. The outstanding performance is verified by means of realistic simulations, in which three state-of-the-art data dissemination protocols are selected to compare with TrAD. On one hand, TrAD can fit the irregular road topology and considers both road traffic and network traffic status. On the other hand, TrAD uses double suppression mechanisms to limit transmissions, which not only performs the broadcast suppression technique in well-connected network, but also constrains the redundant rebroadcast of SCF-agent in disconnected network. Therefore, TrAD only consumes moderate transmissions to achieve relatively high data coverage with low delay even though suffering GPS drift. For the future work, we plan to implement TrAD protocol on our Wireless Multimedia Sensor Board (MiLive) 5 to perform the real world measurement for specific applications of VANETs.

Fig. 1 .

 1 Fig.1. Illustration of TrAD protocol.

  , N8}; C 5 ={N9, N10}; C 6 ={N11, N12}. Vehicles in the same direction with respect to S are exactly classified into the same directional cluster. Additionally, the classification result of AMD is also illustrated. AMD divides the radio range into 4 equal sectors (D1, D2, D3 and D4). However, the classification result is not accurate. The C 2 ={N3, N4} and C 3 ={N5, N6} are merged into sector D1, and the C 5 ={N9, N10} and C 6 ={N11, N12} are combined into sector D3. Moreover, vehicles N3 and N9 are ambiguous on the boundary of sectors. The result proves that the directional sector classification of AMD is inflexible and inadaptable for irregular road topology. b) Traffic adaptive sorting technique This technique considers both road traffic and network traffic status, which includes three components: the number of neighbors and the distance between sender and neighbor represent the road traffic condition, and the Channel Busy Ratio reflects the network traffic status. The metric of the number of neighbors (Eq.1) indicates the coverage of potential vehicles in the next transmission. #Neighbor is the instantaneous number of neighbors in neighbor list. max. #Neighbor is a preset maximum number of neighbors.

  min

Fig. 2 .

 2 Fig.2. The operation of TrAD protocol flowchart

Fig. 3 .

 3 Fig.3. Maps: (a) Manhattan borough of New York City (USA). (b) Clermont-Ferrand (France)

Fig. 4 .

 4 Fig.4. Result in Clermont-Ferrand scenarios with uniformly distributed traffic route: (a) PDR, (b) Number of Transmissions, (c) Delay.

Fig. 5 .

 5 Fig.5. Results in Manhattan scenarios with uniformly distributed traffic route (traffic route 1): (a) PDR, (b) Number of Transmissions, (c) Delay.

Fig. 6 .Fig. 7 .

 67 Fig.6. Results in Manhattan scenarios with non-uniformly distributed traffic route (traffic route 2): (a) PDR, (b) Number of Transmissions, (c) Delay.

  To count the traffic flow, inductive loop detectors are deployed under every lane. The unit of traffic flow represents the number of vehicles passed the detection point per hour (vph). We set 5 levels of traffic flow that include 450 vph, 896.4 vph, 1353.6 vph, 1803.6 vph and 2257.2 vph.

Fig. 8 .

 8 Fig.8. Results in Highway Scenario: (a) PDR, (b) Number of Transmissions, (c) Delay.

Fig. 9 .

 9 Fig.9. The impact of GPS drift on TrAD in Clermont-Ferrand.

Fig. 10 .

 10 Fig.10. The impact of 50m GPS drift on TrAD, AMD and UV-CAST.

ACKNOWLEDGMENT

This work has been sponsored by the French government research program "Investissements d'avenir" through the IMobS3 Laboratory of Excellence (ANR-10-LABX-16-01), by the European Union through the program "Regional competitiveness and employment 2007-2013" (ERDF-Auvergne region), and by the Auvergne region.

Thanks to Philippe Vaslin (Associate Professor, Ph.D.) and Jean Connier (Ph.D. student) for revising the manuscript of this paper. Thanks to Dongsheng Yan (M.E.) and Philip Dechant (M.E.) for creating the realistic scenarios.


Boudi Zakaryae
email: zakaryae.boudi@gmail.comel-miloudi.el-koursi

El Koursi

El Miloudi

Collart-Simon Dutilleul

Khaddour Moha
email: khaddour@oncf.ma

High level Petri net modeling For railway safety critical scenarios

This paper presents a high level Petri net modeling methodology for railway systems safety critical scenarios. This methodology is based on modular high level Petri nets, including most relevant components of a railway safety system, as safety regulation procedures, interlocking and especially human factors, enabling more various gathering of information and allowing the study of diverse possibilities in a same global model. At first, this method was applied to the infrastructure and the regulation of the Moroccan Kenitra railway station. Afterward, a first approach of real accident scenario modeling was introduced for "Saint Romain en Gier" accident, taking account of human errors. Our goal is to bring formal tools in order to study weaknesses in shunting management within train stations and accidental scenarios, considering automatic mechanisms and human involvement.

Introduction

Signaling systems are heritage of railway history and may be multiple within the same country and from a country to another. In order to face this problem, a new control system "ERTMS/ETCS" (European Rail Traffic Management System/European Train Control System) is destined to become a common standard in Europe. For more complex areas, ground and other logical signaling rules defined within a national context such as the "pink instruction" (Consigne Rose) are used. They prevent any conflicting use of a given infrastructure.

Achieving railway interoperability allowed by ERTMS requires a common understanding of requirements by all involved parties. In this context a formal rigorous model is an effective tool to identify and clarify ambiguities. The PERFECT project (Performing Enhanced Rail Formal Engineering Constraints Traceability) is a French scientific project that aims to formalize railway specifications and validate various systems. Systems Models will be developed and formally studied to determine, if possible, the compliance between ERTMS and National railway specifications [START_REF] Bon | Study of implementation of ERTMS with respect to French national rules using a B centred methodology[END_REF].

In this context, railway safety research as the PERFECT project one is facing the complexity of real railway scenario modeling. In fact, railway safety systems are semiautomatic systems, including human manipulation, infrastructure requirements and automatic actions (interlocking, ERTMS control…), yet, it requests a full and comprehensive modeling, as expressed by so many researchers and operators [START_REF] Belmonte | Interdisciplinary safety analysis of complex socio technological systems based on the functional resonance accident model: An application to railway traffic supervision[END_REF]. One of the ultimate goals of this study is to bring a modular modeling methodology that is able to allow different experts and specialists of various fields to contribute independently in the scenario global model, especially for human factor.

High level Petri nets modeling methodology

Petri nets (PN) are a powerful tool for studying a wide variety of discrete event systems. They are useful for both static modeling thanks to their structure, and dynamic modeling through their operating rules. In this context some typical cases are modeled using the CPN-tools software platform. This software has been proposed by the PERFECT project, especially for its extensive use in the previous contribution works [START_REF] Jensen | Colored Petri Nets and CPN Tools for modelling and validation of concurrent systems[END_REF], especially in rail systems assessment [4, 5, 6 and 7].

However, the use of Petri nets until now does not cover all a complete railway system modeling needs. Indeed, railway system models should be used by a larger team of diverse railway abilities and skills. The present methodology focuses on creating modular high level Petri nets that separates safety stakeholders in a railway scenario and enables the introduce of human factor, which has an extremely important influence in railway operation. Those sub-models are then gathered using the hierarchical levels of Petri nets. In the following, this method application shows how a railway infrastructure, regulation, interlocking and human action can be represented by a modular approach for high level Petri nets.

Description of Kenitra railway switching station

Kenitra Station is a mainline station connecting this city that is located in the northern suburbs of the capital Rabat to other Moroccan cities through line trains and Rapid shuttles trains (TNR). It also receives and manages freight and work trains. The safety management on these routes for all traffic types is warranted through the PRS switching station [START_REF]ONCF Pink Instruction -signaling plans of Kenitra station -CG S6A n°8[END_REF]. But some movement cases are managed on the ground by safety agents.

PRS Station of KENITRA enables the control of 45 routes. The controls are handled by the security chief who is also responsible for trains' movement. Some moves require handling safety equipment on the ground via permissions.

The next sections will detail some modeling possibilities of railway station aspects considering the case of Kenitra station. Those models could be injected in larger railway scenarios models which take into account station features.

Modeling an interlocking aspect (a part of route formation)

This section will show the modeling of an interlocking aspect of the Kenitra station. The route formation is materialized by interlocking control of turnouts and, if appropriate, by the actuation of interlocking between routes in opposite directions. It forbids the formation of all incompatible routes and authorizations. In the model constructed (Fig. 1), as in [START_REF] Simon | Performing Enhanced Rail Formal Engineering Constraints Traceability[END_REF][START_REF] Antoni | Formal validation method for computerized railway interlocking systems[END_REF], three basic modes of transport are considered: the permanent plot (TP), automatic destruction (DA) and manual destruction or shunting routes which can't have AD. TP mode is used only on safe routes; therefore, whenever a route requests TP mode, it is necessary to verify that it is not prohibited by that mode. More detailed interlocking information and modeling are presented in [START_REF] Antoni | Formal validation method for computerized railway interlocking systems[END_REF][START_REF] Sun | Formal modeling methodology of French railway interlocking system via HCPN[END_REF]. Still considering Kenitra station, and more specifically, the route "TS-RDS" (fig. 2) of the station, the present section describes the infrastructure and the regulation modeling in the scope of one global model. This model has three hierarchical levels. The first level represents the track section involved in the route (Fig. 3). In this study case, only the nominal direction movement is covered. The Petri net was composed by gathering elementary nets we had established for the infrastructure structures (zones, communication turnouts, signals…). This modular modeling method is one very interesting point in this work.

Fig. 3. First hierarchical level model

The traffic movement is allowed by the opening of the "signal 7". The second hierarchical level details the control of this signal. To simplify the modeling, certain open-ing signal conditions have been shown in a simplified manner. This is the case of authorizations (Fig. 4). Turnouts control processes are modeled in the third level.

Railway safety critical scenario modeling

As an important part of this work, real railway scenario modeling was studied. The concerned modeling methodology was applied to the case of the collision between a work train and a TGV in Saint Romain en Gier accident.

Circumstances and scenario of Saint Romain en Gier accident

The railway line from Lyon to Saint-Étienne is a double-track line, equipped with permanent installations of opposite direction (Installations Permanentes de Contre Sens-IPCS) between Terre Noire and Givors [START_REF] Beatt | Rapport d'enquête technique sur l'accident ferroviaire du 5 avril 2004 à Saint-Romain-en-Gier[END_REF]. IPCS are specific signaling compounds that enable trains movement in the reverse direction. The line is regulated, rail traffic being subject to supervision by a regulator. Figure 5 shows the involved railway infrastructure.

High level Petri nets Scenario Modeling

At first, we have modeled the scenario's related infrastructure, linking Givors and Rive en Gier railway stations. Fig. 6 presents this first level global infrastructure model. The stations are represented with places. Movements between these stations are covered by transitions in the model. The transition "TB -> Givors" is one that includes movements leading to the accident and will be detailed in another hierarchical level. In this section, more detailed modeling of the infrastructure associated to the accident is presented, along with the work train "TTX 4" modeling of movements that led to the collision. Figure 7 shows the model of the accidental movement. The areas are modeled with places as well as signals. Here are introduced two types of transitions to represent the movement of the work train between two areas. One type denoting the "regulatory crossing" signal and a second type for the "illegal crossing". At this point, the sub-model of regulatory crossing conditions of an intermediate signal in the intercepted track is shown. This modeling will visualize the Regulation abuse residing in the not allowed crossing of a signal that we have identified as a direct cause of the accident. Figure 8 illustrates this model for the case of the square signal C11, presented in another hierarchical level of the Petri net. Two main conditions determine the feasibility of such a signal crossing, namely, the establishment of direction in IPCS and bulletin I's authorization or the traffic agent's one. The two mentioned conditions are modeled with places. The next sub-model describes the behavior of the three driving agents of the work train towards the intermediate signal. The reaction of each agent is modeled with a place, and decision making in a transition. If no reaction is to point out, as in the real case of accident Saint Romain en Gier, the transition "decision crossing C11" will be validated (Fig. 9 -case of square C11).

Conclusion and Perspectives

Under the PERFECT project (Performing Enhanced Rail Formal Engineering Constraints Traceability), a similar study to the one proposed on the French LGV-EST railway line is conducted on Kenitra railway station, being part of the future Moroccan high speed railway line linking Tangier and Casablanca within ETCS level 2. This is what makes this study a first interesting international perspective of this project.

In this respect, a modular approach for colored Petri net modeling was introduced for railway infrastructure and scenarios involving human decision, particularly accident scenario in order to provide formal tools both for checking railway rules consistency and accident analysis. This study enables more complete analysis of railway complex situations by creating the possibility of integrating all railway aspects, which could be modeled separately, in one global model. Therefore, this methodology is responding to the needs of railway researchers, experts and operators so that they could work together a complementary way.

Fig. 1 .Fig. 2 .

 12 Fig. 1. Part of route formation

Fig. 4 .

 4 Fig. 4. Second hierarchical level model

Fig. 5 .

 5 Figure 1

Fig. 6 .

 6 Fig. 6. Saint Romain en Gier accident infrastructure model

Fig. 7 .

 7 Fig. 7. Model of movements that led to the collision

Fig. 8 .

 8 Fig. 8. Crossing a signal in terms of regulation

Fig. 9 .

 9 Fig. 9. Driving agents' decision making


Zakaryae Boudi

; El

Miloudi El Koursi

Simon Collart-Duttilleul

Safety critical software construction using CPN modeling and B method's proof

Keywords: Software development, Colored Petri Nets, B method, Formal verification, Railway safety

Meeting the strict safety requirements in critical software development is today crucial for the safety-related industrial environment, especially railways. To be able to prove that all safety properties are captured in the system requirements and software specifications, as well as that the final software product satisfies all specifications, a formal approach is the most convenient. Indeed, the use of formal means of description in the development process was highly recommended by the CENELEC standard. Accordingly, this paper presents by means of a level-crossing gate controller case study, the practical use of Colored Petri Nets transformation into B abstract machines for safety critical software development.

Introduction

Transportation sectors, especially railway systems and aviation, are known to be the primary domains to apply formal methods in software design and validation [START_REF] Havelund | Formal Analysis of the Remote Agent Before and After Flight[END_REF]. For instance, in France, the functional requirements of the SACEM system present in RER Line A in Paris were formally constructed in the B language [START_REF] Guiho | SACEM Software Validation[END_REF] as well as for the automatic train system of the metro line 14 which was the first driverless metro line in Paris [START_REF] Behm | Météor: An Industrial Success in Formal Development[END_REF]. In this context, one of the ultimate goals of this work is to show in a practical manner the benefit from incorporating Colored Petri Nets in safety critical software development process using a conversion methodology aiming to provide B abstract machine preserving all the relevant information contained in the Petri Net models (fig. 1). Thereby, not only the understanding of the system requirements is reinforced, but an additional input is made available for the use of the B method validation tools, particularly when the considered system modeling is much easier using the graphical form of Petri Nets. In fact, the common use of Petri Nets in railway requirements modeling within industrial and research stakeholders [4, 5, and 6] makes the combination of their graphical modeling power and the B formal verification tools very interesting.

Fig. 1. Waterfall CPN to B development process

The transformation rules of the Colored Petri Nets conversion to the B abstract machines are developed as part of the national French project called "PERFECT" (Performing Enhanced Rail Formal Engineering Constraints Traceability) [START_REF] Lanusse | Preliminary methodological propositions for certification support using formal methods within a model driven approach (Propositions méthodologiques préliminaires pour le support à la certification à l'aide de méthodes formelles selon une approche orienté modèles)[END_REF]. This scientific research project focus is to provide formal techniques and approaches in order to determine, if possible, the compliance between the ERTMS (European Rail Traffic Management System) specifications and National railway safety properties.

At first, this paper will introduce the study case example consisting of a level crossing gate controller and present the corresponding CPN model. Afterwards, the obtained B machines will be described and the safety properties introduction and verification will be shown. Finally, some interesting perspectives of this work will be stated.

Case-study: LX gate controller 2.1 Description

The example we consider in this work, for the matter of space and clearness, consists of a simple level-crossing gate controlling device. The level-crossing gate can have only two possible states; it is either up or down. Apart from the case of a train passage, the normal gate position is the "up" one. When an arriving-train is detected through the train-arriving sensor, which is launched by the passage of the train toward the level-crossing, the controlling device commands the lowering of the gate. As soon as the rolling away of the train, the leaving-train sensor is launched and the controller commands the raising of the safety gate. Of course, the safety property to check in this example is that the gate has to be in the "down" position if a train is present in the danger zone, i.e., between the two sensors.

LX gate controller CPN model

As presented in figure 2, the system specification is modeled using a Colored Petri Net. The modeling tool used in this study is the CPN-tools software platform. The main architects behind the tool are Kurt Jensen, Soren Christensen, Lars M. Kristensen, and Michael Westergaard [9].

The corresponding B machine

Each Place of the CPN model has some corresponding variables in the output B abstract machine. These variables denote the state of the place and the occurrence of the tokens in it. Similarly, the transitions states are also represented using variables in the B machine. The evolution of Petri Net states is mapped using the B machine operations. Figure 3 shows extracts from the obtained B machine. The next section will describe the use of the latter in safety properties verification.

Safety properties verification

The first property to check is that the invariant corresponding to the Petri Net place representing the level-crossing gate is always taking one state at the time, either up or down. The B invariant of such property is expressed by the following expression:

(occ_up_Gate + occ_down_Gate=1) Furthermore, the safety invariant relevant to the gate position when a train is within the levelcrossing area is expressed by:

(occ_train_LevelCrossing =1 => occ_down_Gate =1)
This invariant denotes also that the arriving sensor is launched by the rolling of a train. Theses invariants are added to the "INVARIANTS" Fig. 3. Extracts from the obtained B abstract machine clause in the obtained B machines. Thereby, the model checking of the B method tools "ProB" and "Atelier B" proof are applied to the machine. In this example, both tools validated the trueness of the system safety design since the proof had reached a rate of 100% (fig. 4).

Conclusion and future work

In the scope of safety critical software development, this paper presented, through a case study, the practical use of the combination of Colored Petri Nets and the B method in the verification process. The present work, as part of the PERFECT project, has a number of perspectives; the first one is the development of an elaborate transformation rules catalogue and the definition of a refinement methodology applicable to the obtained B machines. Besides, the project aims at providing a tool for the automatic transformation based on the theoretical rules.

Fig. 2 .

 2 Fig. 2. Level-crossing gate controller CPN model

Fig. 3 .

 3 Fig. 3. "Atelier B" Proof results


Julian Murgia

Cyril Meurie

Yassine Ruichek

All-Day moving objects detection for security at level crossing

Keywords: Color invariants, background subtraction, tracking, moving objects detection Résumé invariants couleur, soustraction d'arrière-plan, suivi, détection objets mobiles

teaching and research institutions in France or abroad, or from public or private research centers.

Introduction

Since many years, level-crossings (LC) are considered as sources of many incidents. In 2009, "Réseau Ferré de France" counted 36 fatal accidents (30 dead persons in vehicles and 6 pedestrians killed), and 120 collisions involving trains. In the majority of cases, the accident is caused by carelessness such as the non-compliance with a stop sign, a baffle passage between lowered barriers, rides between lanes in traffic jams implying stationarity on the LC, excessive speed of approach, little visibility around the LC, etc.

Several actions were led to increase security at level-crossing. The ANR project PANsafer 2009-2012 is one of these actions (Salmane et al., 2012a[START_REF] Salmane | Using hidden markov model and dempstershafer theory for evaluating and detecting dangerous situations in level crossing environments[END_REF]. It proposed to develop and evaluate an intelligent, communicant system that allows the detection (thanks to a video perception module) of potentially dangerous situations occurring at level-crossing environment. Then, thanks to an infrastructure/vehicles communication module, it transmits a criticity information to all concerned actors (road users, train, centralized command station, etc).

The video perception module has to be able to work any moment of day. Common foreground segmentation methods from the literature seem to be adapted to this problematic, but they are very sensitive to important luminosity changes, which are amplified when data is acquired on a whole day from daybreak to twilight. These methods generally adapt themselves to the changes in the scene by updating their background model. Even if this update is needed to take into account the presence of mobile objects that actually stay immobile for a certain time (such as parked cars), this operation is costful in resources and time. As an example the Mixture of Gaussians method [START_REF] Stauffer | Adaptive background mixture models for real-time tracking[END_REF] needs 12 to 20 seconds to be fully adapted, according to our observations. During this amount of time, resulting segmentations include huge parts of the background as false positives, which make them difficult to use for another process. Other well-known BGS methods suffer from such problems, each one managing them with different efficiencies. In this context, we propose to increase the robustness of the global system by proposing a better mobile objects detection with the joint use of background/foreground segmentation and color invariants and/or color spaces. Resulting segmentations are purposely obtained with mismatching learning images and process images, causing unadapted learnings, in order to simulate the effects that need to be addressed.

To demonstrate the effectiveness of the proposed strategy and go further in the proposed strategy, a moving objects tracking algorithm is applied on resulting segmentations.

Method

Color invariance

Prior to background subtraction, each image is firstly simplified with a colorimetric invariant. Color invariance consists in determining the illuminant color of the image before "normalization" in order to obtain an image that theoretically displays the scene under a canonic illuminant, whatever the time of the day. Multiple methods exist in the literature to perform this task [START_REF] Obdrzalek | On the interaction between object recognition and colour constancy[END_REF]: some make use of low-level features (low-level statistic of physics-based), some make use of a learning phase, and finally some are a combination of both. However, each method doesn't produce the same result and none can be considered as "universally true". [START_REF] Gijsenij | Color constancy using natural image statistics and scene semantics[END_REF] showed that certain operators were performing better on certain images than others. In this way, an algorithm was also proposed by [START_REF] Bianco | Automatic color constancy algorithm selection and combination[END_REF] to detect the best color invariant that fits to a given image. This is the reason why multiple operators were used : Chromaticity space (also known as Normalized RGB), Greyworld normalization [START_REF] Buchsbaum | A spatial processor model for object colour perception[END_REF], Comprehensive normalization [START_REF] Finlayson | Comprehensive colour image normalization[END_REF], Affine normalization [START_REF] Obdrzalek | On the interaction between object recognition and colour constancy[END_REF], c1c2c3, m1m2m3 and l1l2l3 color spaces [START_REF] Gevers | Color-based object recognition[END_REF], RGB-Rank [START_REF] Finlayson | Illuminant and device invariant colour using histogram equalisation[END_REF], YIQ color space [START_REF] Walter | Color TV Servicing, third edition[END_REF], YCbCr color space, YCh1Ch2 color space [START_REF] Carron | Segmentation d'images couleur dans la base Teinte Luminance Saturation : approche numerique et symbolique[END_REF] and CIE L*a*b* color space.

Background subtraction

Background subtraction techniques consist in creating a background model which is then used to perform a comparison between this model and an input image, resulting in a segmentation representing parts of the input images that do not belong to the background. Multiple techniques exist in the literature [START_REF] Bouwmans | Statistical background modeling for foreground detection: A survey[END_REF], such as the well-known Mixture of Gaussians algorithm proposed by [START_REF] Stauffer | Adaptive background mixture models for real-time tracking[END_REF] which consists in a multi-modal distribution of gaussians for each pixel, which allows the modeling of more complex backgrounds. This method is parametric, which led us to choose another method to perform our method.

The Codebook algorithm proposed by [START_REF] Kim | Background modeling and substraction by codebook construction[END_REF] is a well-known method that performs a background subtraction on images taken from a still point of view in order to segment moving objects out of the background.

It showed very good results on images containing many common difficult environments such as tree foliages, flags, fountains, sea shores as well as small illumination changes. This method works in two distinct phases : learning and processing.

The Learning step builds a model representing the background of learning images. This model consists in N codebooks (1 per pixel) containing various codewords that are computed (or updated) at each learning iteration when the pixel is supposed to be part of the background. More precisely, these codewords are defined by two vectors : the first one contains the RGB colour of the pixel, the second one contains multiple statistical and temporal data (such as minimum and maximum brightness, frequency and time of occurrence, MNRL...).

During this step, a codeword is integrated (or updated) to the model if it satisfies two conditions : a) brightness distortion constraint and b) color distortion constraint.

a) The intensity of the pixel must lie in the interval [Imin ; Imax] determined from all the minimum and maximum brightnesses observed for this pixel. This range of brightness delimits the range under which a codeword is considered as shadow, and above which it is considered as highlight. b) The color distortion colordist, calculated from the pixel xt and the tested codeword vi from the model, must lie under a given threshold ε. Following Formulas (1) and ( 2) define the calculation of color distorsion.

(1)

(2)

At the end of this step, the model is cleaned of the codewords that were most probably belonging to foreground.

To do so, the algorithm make use of each codeword's MNRL (Maximum Negative Run-Length) value defined as the longest interval during the learning period that the codeword has not recurred. A low value means that the codeword was frequently observed. A high value means that it was less frequently observed and that it should be removed from the model as it was probably part of foreground. The MNRL is different from the frequency value because even if a pixel has a large frequency and a large MNRL, then it is most probably a foreground object that stayed still for a moment.

The Processing step that follows performs almost the same task as Learning. It simply consists in verifying the existence of a corresponding codeword in the model for the tested pixel of the processed image. To do so, the same procedure is applied to each pixel (application of the two constraints described above). If a match exists for this codeword in the model, then it means that the pixel belongs to the background. If no match is found, it means that this pixel is part of the foreground.

The general algorithm described by Kim et al updates the model during processing step to take into account small illumination changes in the image. In opposition to the general algorithm, we do not update the model during this step because we assume small (and important as well) illumination and color changes to be already dealt by the use of a colorimetric invariant. Thus, our method uses a unique background model to deal with allday images and spares resources needed by the update of the model.

Multiple objects tracking

In order to show the advantages gained by the use of our method, we applied a multi-point tracking to the segmentations obtained. Tracked points are the barycenter of each region. This tracker consists in the generation

colordist  x t ,v i  =   x t  2 -p 2 p 2 = 〈x t , v t 〉 2 ||v i ||
and/or the update at each frame of a Kalman Filter with the closest detected position to each Kalman Filter's prediction. The tracking system then draws a frame around each successfully tracked region.

Since our objective is not to perform a better tracking but a better detection of moving objects in complex conditions, we do not take into account loss of tracking. As moving objects can be aligned to the camera point of view, this case causes the two regions to be melted into one, which instantly causes loss of tracking. Despite this drawback, one can still easily observe the path of objects at each frame. Results presented in next section will show that in most cases, tracking is extremely poor when our method is not applied.

Experimental results

The first base: an urban scene

This method was tested on a set of images acquired with a video-surveillance objective. These images were taken at different moments of a day, capturing a similar scenario. Some examples are shown in Fig. 1 (from left to right: 7am, 9.30am, 12.00am, 3.00pm and 4.30pm). One can notice that these images were recorded in winter so days are shorter.

As we can see on Figure 1, if no color invariant is used, and when the learning step was made on different images than those processed, segmentation quality is pretty bad. Many regions of the images are erroneously detected as foreground. The actual foreground appears melted in false positive detections. The use of a colorimetric invariant then removes a lot of false positive detections in the background, while good detection is still performed on foreground. Moreover, even if these results appear better, false detections remain in the distant background.

F-Measures were calculated for both classes (foreground and background). Evaluations were done for each colorimetric invariant and color space (Rank line) in order to determine which one of them performs the best for both classes. Results provided in Tables 1 and2 pointed out RGB-Rank colorimetric invariant to perform the best for this application. This method has been tested on a second base of images sequences acquired at different moments of one day. These images, of size 640x480 were acquired in the PANsafer project with the use of a camera jAI CV-M9CL equipped with a 3-CCD sensor.

Recorded sequences consist in different scenarios reproducing critical situations involving dangerous users behaviours or unintentional blocking on level-crossings. Some of these scenarios consist for example in a vehicle parking on the level-crossing because of a blocking of one or more vehicles before it, or a chicane passage between closes barriers. One can observe small color changes and illumination variations on these images ; unfortunately, the acquisitions were unable to show huge changes in the images. Nevertheless, we chose three images sequences that seemed to show enough differences to apply our method. Thus, the first images base (taken at 12am) named P1 shows a brighter aspect than the second one (taken at 4pm) named P2, and the third one P3 (taken around the same time, but the scene appears darker because of a cloud passing by the sun). From these three processing bases, we define three learning bases of 75 images each, respectively named L1, L2 and L3. From this naming rule, a test involving Learning base L1 and Process base P2 is called "L1_P2". Each test was processed multiple times with different color invariants operators. It is important to recall that for each test case, the same color invariant operator was used for both learning images and processed images. Also, the learning base is never updated after the Codebook Learning step.

Each test was driven with four steps:

• Apply a colorimetric invariant operator on learning base ;

• Build the Codebook model with learning images ;

• Process background subtraction after applying the same colorimetric invariant to processing base ;

• Perform tracking operator on segmented images.

Parameters used for the Codebook algorithms are the following : α = 0.4, β = 1.2, ε1 = 0.2, ε2 = 100. These parameters are empiric values determined by Codebook algorithm authors and that provide good results most of the time, whatever the images.

Evaluation criteria

Resulting segmentations were evaluated with the use of ground truths segmentations. For each segmentation, 4 pixel cases are considered:

• True Positive (TP) : the pixel is correctly classified as foreground ;

• True Negative (TN) : the pixel is correctly classified as background ;

• False Positive (FP) : the pixel is incorrectly classified as foreground ;

• False Negative (FN) : the pixel in incorrectly classified as background.

F-Measure (FMc) value was then calculated for both classes foreground (fg) and background (bg) (Formula (3)).

(3)

Having c either bg or fg. F-Measure Fmc is the harmonic mean of Recall and Precision. The greater the value, the better the segmentation quality. Since our method is meant more to reduce the False Positive Rate (so reducing the number of regions detected as foreground) that to augment the True Positive Rate, we wanted to observe its effects on both classes separately.

Background subtraction results

Since three images sequences seemed to be interesting enough for the tests to be driven, we denote three different tests with a different name for each, which we present the results in Tables 3 and4, the same way they were presented in Part 3.1.. These results are averages obtained with a comparison against ground truth references made at hand by a specialist. A high value denotes a good segmentation quality, 100% being the exact same quality as the reference (for the given class only). column. Forgetting uninteresting results (Chromaticity, c1c2c3, L*a*b, YCbCr), HSL shows better or equivalent results than no-operator used in the case of different learning base and processing base (Lx_Py, with x ≠ y). The same way, RGB-Rank performs very good as well, even if its ranks for background (6 th , 9 th , 6 th ) and foreground (2 nd , 5 th , 5 th ) make it appear in the middle of the rankings, its evaluations lay above 80% for background and close to 5% for foreground.

One can also observe good results obtained with no invariant used ("none" column). F-Measures remain close to HSL ones, which we can explain by the relative similarity of tested images from one base to another. Codebook algorithm uses its own color model (color distortion and brightness distortion), which is also very effective for limited illumination changes. Even with this important quality, one can notice that using Codebook alone does not perform the best at all time, according to our results, which does not invalidate our observations. Color invariant also do not seem to degrade the quality of segmentations, regarding HSL and RGB-Rank results. For any test, F-Measure results with an (interesting) color invariant are whether close to none (with a difference of 5%), whether more than 15% better. ∓ Figure 2 shows some examples of segmentations, all obtained with a learning made on base 1 (L1). First line shows results for an image of processing base 1 (P1), second line shows an image from processing base 2 (P2) and last line an image from base 3 (P3). From left to right, the original image, the ground truths images, a segmentation performed without any colorimetric invariant applied, a segmentation with HSL conversion and lastly a segmentation using RGB-Rank. First line of Figure 2 of course shows the most accurate segmentations. This is normal, since the learning was made on images of the same base as those processed. One can notice that the barriers were not well segmented. Second and third lines are more interesting. It is clearly visible that a segmentation obtained without any colorimetric invariant shows a very high false detection rate. Lots of parts of the background are incorrectly detected as foreground. On the contrary, HSL and RGB-Rank allow the algorithm to reduce dramatically the false positive rate. Of course, these segmentations cannot be perfect, but segmentations like these can now be used for other processes such as tracking, without having to assume as much errors in it. A mathematical morphology post-processing could also be applied to resulting segmentation to remove noise and close holed foreground regions.

Tracking results

This section aims at showing the influence of the colorimetric invariants on the tracking of moving objects. It is important to note that the main objective of our contribution is not to determine which tracking method suits better to a use, but to point out the improvements of a better segmentation for any tracking method. To do so, mathematical morphology operators are applied in order to remove noise and recover full foreground regions in resulting segmentations, then we applied tracking. Such methods need parameters, that depend directly on the image aspect. Because of the inconstancy of the resulting noises from one test to another, it was difficult in certain cases to obtain the best results. Yet, we believe it is possible to obtain segmentations that can give better results. Nevertheless, one can determine the paths of interesting objects.

Figure 3 shows the results of the objects tracking for segmentations obtained on base P1 with a learning on base 1 (L1). The first column shows results on images without colorimetric invariant, second column images are converted to HSL, and third column images use the colorimetric invariant. For each image, one color corresponds to one tracker that followed an object. Figure 4 shows the same results, but for segmentations obtained on base P2, also with a learning on base 1 (L1).

First, to make these images more understandable, we must describe the paths of objects for each sequence. The first and third sequences show closed barriers. Then, a car arrives from the road, top right of the image, turns on the level-crossing and stops on it. On the left of the images, two people wander. The second sequence shows three cars following each other from the horizon, top right. The first car passes the level-crossing, followed by the two others. The last car stops on the rail-crossing as fences go down.

Although, it is very difficult to read these images with the critically high number of active trackers in very noisy segmentations. One can determine certain paths and by comparison, notice that certain cases simply do not work.

Let us focus our attention on the points that should theoretically track the car.

On Figure 3, first image (without colorimetric invariant) shows the path of the car (in white) arriving from the top right road and stopping in the middle of the scene. The second image (in HSL color space) shows two paths following the car. This is caused by the fact that the segmentation of the car is decomposed into two regions. The third image (with RGB-Rank) shows a trajectory, but with a color that changes, and which means the tracking was lost and recovered after a while. One can point out of this image that in this specific case (learning and process bases are the same), the tracking quality is not very affected by the use of a color invariant or not, which means we do not degrade or lose information with this technique.

On Figure 4, first image (without colorimetric invariant) shows only a few regions in the left could be tracked, but no car could be since we can't determine any path in the center of the scene. On the second and third images, though, one can observe many points in curves on the theoretic path of cars, so these ones could be tracked.

Two hypothesis come out from these observations. First, the quality of the segmentation obtained is critical for the quality of the tracking. A noisy segmentation can cause poor tracking as can be seen in Figure 4, 3 rd image. Second, we point the decisive effect of the use of a colorimetric invariant for the final goal of getting a tracking. As seen on Figure 4, 1 st image, no tracking was possible if no colorimetric invariant was used. On the contrary, the use of either HSL or RGB-Rank enabled the tracker to draw the trajectory of moving objects, even if the number of false detections remained high.

Conclusions & Future work

In this paper, we showed the usefulness of the joint use of a colorimetric invariant with a codebook-based background subtraction system which is adapted for fixed-camera applications. This strategy allows in one hand rarefy the expensive background model update operation of the background subtraction algorithm, and in second hand to provide better results with a colorimetric invariant than without. Two color invariants behaved particularly better than other for this application : HSL and RGB-Rank. Multiple conclusions can be done from this observation: first, RGB-Rank is a good choice as a color invariant for this kind of process as it performed well in almost all cases; second, the difference between previous and current results for HSL color space confirm that results of this methods are highly dependent of the type of camera and the time of observation.

Plus, this method was successfully used with a simple multi-point tracking in order to follow targets during time.

Even if this tracker was not the most elaborated one, a tracking could be done in most cases on resulting images. This is a very interesting result for level-crossing surveillance and dangerous situations detection, since a tracking of mobile objects can be performed successfully with better chances of success, especially when the background model is not yet adapted to an important change in the scene. We showed that even a very simple tracking method is able to provide at least the shape of objects trajectories when segmentations are obtained through our method, where it is unable to do so when they are not. Applied to existing dangerousness-estimation methods involving even better tracking solutions, this technique may provide very good results for a lower cost. These observations are motivating to improve the codebook with different ways to deal with illumination and color changes according to any situation. Images in this paper were just different enough to confirm the efficiency of this method, but other images with specific, very dark or rainy situations should be interesting to test as well.

Future works on the problematic of detection can be done, especially on a new integrated color model included in the Codebook algorithm. Also, tests should be done on images showing darker and/or different meteorologic conditions to observe the behavior of this method on harder conditions of observation.

Fig 1 :

 1 Fig 1: Segmentations obtained images for each time of day (with a learning on 7.30). a) initial images in RGB color space b) ground truths images c) without colorimetric invariant d) with RGB-Rank

  FM bg Affine Norm c1c2c3 Chromaticity Compr. Norm Greyworld HSL L*a*b* l1l2l3

Fig 2 :

 2 Fig 2: Segmentations obtained with a learning base L1. From left to right : original image, ground truths images, without colorimetric invariant, with HSL, with RGB-Rank

Fig 3 :

 3 Fig 3: Results of objects tracking on segmentations from base P1 with learning on base L1, without invariant (column 1), HSL (column 2) and RGB-Rank (column 3). Red lines correspond to reference path.

Fig 4 :

 4 Fig 4: Results of objects tracking on segmentations from base P2 with learning on base L1, without invariant (column 1), HSL (column 2) and RGB-Rank (column 3). Red lines correspond to reference path.

Table 1 :

 1 F-Measures on background class for every color invariant/space tested.

Table 2 :

 2 F-Measures on foreground class for every color invariant/space tested.

m1m2m3 none RGB-rank YcbCr YIQ

	L1_P1	91,2%	91,4%	91,4%	91,6%	91,6%	91,2% 91,4% 90,9%	89,8%	91,6%	91,5%	91,4% 91,4%
	L1_P2	78,3%	87,9%	90,4%	86,6%	86,1%	81,2% 90,9% 74,4%	79,4%	86,3%	88,1%	90,3% 90,2%
	L1_P3	66,0%	89,0%	90,9%	34,8%	29,3%	66,5% 91,4% 69,2%	77,6%	27,8%	87,9%	81,3% 78,3%
	L1_P4	76,9%	91,5%	92,5%	83,1%	83,6%	69,7% 92,6% 70,1%	77,4%	84,0%	88,0%	92,2% 91,8%
	L1_P5	74,6%	92,1%	92,5%	71,4%	46,6%	88,3% 92,0% 69,7%	77,5%	47,3%	87,3%	92,0% 91,4%
	Mean L1	77,4%	90,4%	91,6%	73,5%	67,4%	79,4% 91,7% 74,8%	80,3%	67,4%	88,6%	89,5% 88,6%
	Rank L1	9	3	2	11	12	8	1	10	7	13	6	4	5
	L2_P2	90,5%	91,1%	91,1%	91,4%	91,4%	91,1% 91,2% 91,3%	89,8%	91,4%	91,0%	91,1% 91,1%
	L2_P3	66,2%	91,2%	91,2%	23,6%	13,8%	79,9% 91,4% 27,4%	80,7%	13,6%	88,5%	87,9% 83,5%
	L2_P4	77,0%	92,8%	92,8%	81,1%	85,7%	69,3% 92,8% 26,9%	82,6%	86,2%	87,8%	92,6% 92,6%
	L2_P5	73,9%	92,6%	92,6%	60,1%	19,8%	84,6% 92,6% 22,2%	83,4%	20,4%	86,1%	91,5% 86,2%
	Mean L2	76,9%	91,9%	91,9%	64,0%	52,7%	81,2% 92,0% 42,0%	84,1%	52,9%	88,4%	90,8% 88,4%
	Rank L2	9	2	3	10	12	8	1	13	7	11	6	4	5
	L3_P3	90,2%	91,2%	91,9%	91,8%	91,8%	89,6% 91,9% 90,8%	90,2%	91,8%	89,2%	91,5% 91,5%
	L3_P4	68,0%	92,8%	91,2%	2,7%	2,2%	74,4% 93,0% 26,9%	76,7%	2,2%	86,6%	87,8% 89,6%
	L3_P5	66,4%	92,6%	92,8%	3,5%	0,1%	81,4% 87,5% 74,2%	68,9%	0,2%	81,5%	66,8% 76,8%
	Mean L3	74,9%	92,2%	92,0%	32,7%	31,4%	81,8% 90,8% 64,0%	78,6%	31,4%	85,8%	82,0% 86,0%
	Rank L3	9	1	2	11	12	7	3	10	8	13	5	6	4
	L4_P4	92,6%	92,8%	92,8%	93,2%	93,2%	92,7% 92,8% 90,8%	91,8%	93,2%	92,7%	92,8% 92,8%
	L4_P5	80,3%	92,6%	92,6%	67,7%	21,1%	79,3% 92,6% 80,0%	81,4%	21,3%	88,3%	91,0% 91,8%
	Mean L4	86,5%	92,7%	92,7%	80,5%	57,1%	86,0% 92,7% 85,4%	86,6%	57,2%	90,5%	91,9% 92,3%
	Rank L4	8	3	2	11	13	9	1	10	7	12	6	5	4
	L5_P5	86,1%	92,7%	92,6%	61,2%	93,1%	92,0% 92,7% 90,5%	90,8%	93,1%	92,8%	92,6% 92,6%
	Rank L5	12	5	6	13	2	9	4	11	10	1	3	7	8
	FM fg	Affine Norm	c1c2c3	Chromaticity Compr. Norm Greyworld	HSL L*a*b* l1l2l3 m1m2m3 none	RGB-rank YcbCr	YIQ
	L1_P1	33,6%	0,6%	0,0%	63,8%	64,6%	1,6%	1,9%	8,5%	6,3%	62,7%	56,6%	0,4%	0,4%
	L1_P2	6,2%	3,1%	2,0%	13,2%	12,6%	4,5% 10,8% 4,2%	4,2%	13,0%	16,9%	2,0%	1,9%
	L1_P3	8,4%	5,5%	6,0%	4,9%	4,6%	3,6% 14,5% 6,5%	7,6%	4,6%	18,2%	4,7%	4,3%
	L1_P4	5,7%	4,1%	3,0%	8,7%	9,0%	1,0% 13,2% 2,6%	3,4%	9,2%	13,3%	1,3%	1,9%
	L1_P5	5,6%	6,1%	3,5%	8,7%	3,7%	2,4% 12,4% 3,1%	4,2%	3,8%	14,5%	2,4%	1,9%
	Mean L1	11,9%	3,9%	2,9%	19,8%	18,9%	2,6% 10,6% 5,0%	5,1%	18,6%	23,9%	2,2%	2,1%
	Rank L1	5	9	10	2	3	11	6	8	7	4	1	12	13
	L2_P2	36,2%	7,5%	2,2%	74,4%	74,5%	19,5% 20,0% 56,6%	20,2%	74,7%	48,0%	5,2%	5,1%
	L2_P3	8,0%	6,9%	3,3%	4,7%	4,3%	5,9% 17,0% 5,4%	8,6%	4,3%	21,2%	7,6%	4,8%
	L2_P4	5,3%	9,2%	5,0%	7,6%	10,8%	1,4% 17,8% 2,2%	4,7%	11,2%	13,0%	2,0%	2,1%
	L2_P5	5,8%	8,7%	2,5%	7,8%	2,9%	2,5% 15,7% 2,7%	9,7%	2,9%	12,8%	1,4%	1,9%
	Mean L2	13,8%	8,1%	3,3%	23,6%	23,1%	7,3% 17,6% 16,7%	10,8%	23,3%	23,7%	4,1%	3,5%
	Rank L2	7	9	13	2	4	10	5	6	8	3	1	11	12
	L3_P3	48,5%	6,5%	3,6%	65,0%	64,8%	16,1% 44,5% 27,3%	26,4%	64,9%	52,7%	33,4% 29,9%
	L3_P4	4,4%	8,3%	4,4%	2,2%	2,2%	2,2% 46,3% 2,2%	3,4%	2,2%	10,7%	8,5%	7,3%
	L3_P5	5,1%	7,7%	2,6%	2,7%	2,7%	2,8% 28,0% 2,6%	4,1%	2,7%	8,5%	5,1%	6,9%
	Mean L3	19,3%	7,5%	3,5%	23,3%	23,2%	7,0% 39,6% 10,7%	11,3%	23,2%	24,0%	15,7% 14,7%
	Rank L3	6	11	13	3	5	12	1	10	9	4	2	7	8
	L4_P4	45,0%	7,7%	3,8%	78,9%	78,8%	26,8% 15,7% 10,3%	21,2%	78,8%	46,8%	1,4%	1,4%
	L4_P5	8,3%	6,7%	2,4%	17,8%	2,9%	3,5% 22,5% 2,5%	6,0%	2,9%	16,7%	2,1%	1,7%
	Mean L4	26,7%	7,2%	3,1%	48,4%	40,8%	15,1% 19,1% 6,4%	13,6%	40,8%	31,7%	1,7%	1,5%
	Rank L4	5	9	11	1	2	7	6	10	8	3	4	12	13
	L5_P5	33,2%	2,6%	0,5%	52,8%	78,9%	9,2% 16,0% 12,9%	19,0%	78,6%	56,5%	4,2%	2,5%
	Rank L5	5	11	13	4	1	9	7	8	6	2	3	10	12

Table 3

 3 Recall c . Precision c 

	Recall fg =	TP TP+FN	, Recall bg =	TN TN+FP
	Precision fg =	TP TP+FP	, Precision bg =	TN TN+FN
	FM c =	2 .  Recall c +Precision c

shows F-Measures obtained for background. One can observe that, similarly to previous results, Chromaticity space color invariant shows best results. But, a look at values in table 4 denotes very poor detection rates in foreground class, which means that segmented images are almost all black. Interesting results lie in HSL

Table 3 :

 3 F-Measures values for the background class

	FM bg Affine Norm c1c2c3 Chromaticity Compr. Norm Greyworld	HSL	L*a*b*	l1l2l3 m1m2m3	none RGB-rank YcbCr	YIQ
	L1_P1	93,01%	94,12%	94,12%	94,34%	94,34%	94,19% 93,35% 94,02%	92,78%	94,34% 94,30% 48,53% 91,59%
	L1_P2	81,63%	96,43%	96,43%	58,05%	58,66%	96,35% 95,69% 83,31%	91,52%	61,04% 92,50% 25,00% 95,45%
	L1_P3	78,06%	94,27%	94,30%	75,37%	78,44%	90,46% 93,28% 87,56%	84,53%	79,50% 83,70% 20,41% 90,27%
	Mean L1	79,8%	95,4%	95,4%	66,7%	68,5%	93,4%	94,5%	85,4%	88,0%	70,3%	88,1%	22,7%	92,9%
	Rank L1	9	2	1	12	11	4	3	8	7	10	6	13	5
	L2_P1	78,73%	94,13%	94,12%	93,49%	93,59%	93,87% 89,48% 88,88%	91,62%	93,58% 90,30%	3,90% 19,85%
	L2_P2	95,51%	96,43%	96,43%	96,69%	96,68%	96,67% 95,60% 96,53%	95,00%	96,68% 96,55% 49,39% 88,25%
	L2_P3	78,11%	94,30%	94,33%	91,75%	91,88%	90,98% 91,07% 78,81%	85,76%	91,99% 84,06%	6,52% 68,00%
	Mean L2	78,4%	94,2%	94,2%	92,6%	92,7%	92,4%	90,3%	83,8%	88,7%	92,8%	87,2%	5,2%	43,9%
	Rank L2	11	2	1	5	4	6	7	10	8	3	9	13	12
	L3_P1	81,15%	94,01%	94,07%	91,17%	91,27%	92,02% 92,19% 86,21%	83,54%	91,41% 84,73% 12,39% 84,89%
	L3_P2	82,63%	96,14%	96,35%	76,74%	76,80%	94,77% 94,89% 66,22%	83,67%	78,24% 85,50%	6,76% 92,87%
	L3_P3	85,66%	94,35%	94,36%	91,51%	92,82%	94,14% 93,59% 92,90%	92,33%	93,16% 93,87% 55,81% 94,18%
	Mean L3	81,9%	95,1%	95,2%	84,0%	84,0%	93,4%	93,5%	76,2%	83,6%	84,8%	85,1%	9,6%	88,9%
	Rank L3	11	2	1	9	8	4	3	12	10	7	6	13	5

Table 4 :

 4 F-Measures values for the foreground class

	FM fg Affine Norm c1c2c3 Chromaticity Compr. Norm Greyworld	HSL	L*a*b* l1l2l3	m1m2m3	none RGB-rank YcbCr	YIQ
	L1_P1	14,64%	0,85%	0,28%	31,12%	31,29%	23,51% 2,87% 28,07%	9,11%	31,80%	28,71% 4,63% 10,79%
	L1_P2	7,69%	2,33%	1,44%	3,49%	3,50%	25,80% 4,74% 8,36%	11,82%	3,60%	14,06% 5,14% 24,56%
	L1_P3	3,55%	5,99%	4,33%	4,09%	4,42%	9,29% 8,25% 5,61%	3,23%	4,62%	4,28%	2,77% 9,55%
	Mean L1	5,6%	4,2%	2,9%	3,8%	4,0%	17,5%	6,5%	7,0%	7,5%	4,1%	9,2%	4,0%	17,1%
	Rank L1	7	8	13	12	10	1	6	5	4	9	3	11	2
	L2_P1	6,18%	4,15%	1,32%	35,02%	36,27%	34,70% 9,78% 9,35%	9,84%	35,66%	13,36% 3,42% 2,20%
	L2_P2	18,41%	1,79%	1,15%	29,11%	28,99%	21,47% 9,55% 25,41%	14,88%	28,53%	26,28% 5,99% 6,93%
	L2_P3	4,53%	5,72%	3,05%	15,93%	16,32%	11,07% 9,69% 3,04%	3,55%	16,76%	4,92%	2,53% 3,94%
	Mean L2	5,4%	4,9%	2,2%	25,5%	26,3%	22,9%	9,7%	6,2%	6,7%	26,2%	9,1%	3,0%	3,1%
	Rank L2	9	10	13	3	1	4	5	8	7	2	6	12	11
	L3_P1	4,91%	1,87%	0,45%	14,78%	14,96%	15,79% 5,16% 7,86%	2,97%	15,44%	6,86%	3,56% 6,11%
	L3_P2	8,46%	2,18%	1,60%	5,19%	5,18%	19,36% 3,43% 5,09%	6,28%	5,40%	7,52%	4,66% 14,78%
	L3_P3	3,81%	1,23%	0,77%	15,50%	18,99%	18,97% 2,54% 11,21%	5,44%	20,28%	21,54% 3,78% 20,11%
	Mean L3	6,7%	2,0%	1,0%	10,0%	10,1%	17,6%	4,3%	6,5%	4,6%	10,4%	7,2%	4,1%	10,4%
	Rank L3	7	12	13	5	4	1	10	8	9	3	6	11	2