Giovani B Vitor

Alessandro C Victorino

Janito V Ferreira

A probabilistic distribution approach for the classification of urban roads in complex environments

Keywords: Road Detection, Computer Vision, Joint Boosting, Texton Map, Dispton Map, Watershed Transform

Navigation in urban environments has been receiving considerable attention over the past few years, especially for self-driving cars. Road detection for Autonomous Systems, and also for ADAS (Advanced Driving Assistance Systems) remains a major challenging in inner-city scenarios motivated by the high complexity in scene layout with unmarked or weakly marked roads and poor lightning conditions. This paper introduces a novel method that creates a classifier based on a set of probability distribution. The classifier, created using a Joint Boosting algorithm, aims at detecting semantic information in roads. This approach is composed of a set of parallel processes to calculate the superpixel using the Watershed Transform method and the construction of feature maps based on Textons and Disptons. As a result, a set of probability distribution is generated. It will be used as an input to model the week classifier by our Joint Boosting algorithm. The experimental results using the Urban-Kitty benchmark are comparable to the state-of-the-art approaches and can largely improve the effectiveness of the detection in several conditions.

I. INTRODUCTION

Autonomous Navigation for urban environments has been receiving considerable attention from the robotic community over the past few years, motivating researchers to propose approaches towards the detection of roads in challenging inner-city environments. The inner-city road detection usually helps an Intelligent Vehicle System to get a better understanding of the environment improving interlinked or dependent tasks such as path planning [START_REF] Rawashdeh | Mult-sensor input path planning for an autonomous ground vehicle[END_REF], road following [START_REF] Thorpe | Vision and navigation for the carnegie-mellon navlab[END_REF], and visual servoing [START_REF] Karasev | Depth invariant visual servoing[END_REF].

Applications for road detection using camera sensors must deal with a set of problems such as: (i) continuously changing backgrounds in different environments (inner-city, highway, off-road), (ii) different road types (shape and color), (iii) the presence of different objects (signs, vehicles, pedestrian) and (iv) differences in imaging conditions (variation of illumination and weather conditions).

Many researchers have addressed this problem using monocular or stereo vision [START_REF] Alvarez | Illuminant-invariant modelbased road segmentation[END_REF] [START_REF] Dornaika | A new framework for stereo sensor pose through road segmentation and registration[END_REF]. Approaches using monocular vision aim at detecting lane marking [START_REF] Felisa | Robust monocular lane detection in urban environments[END_REF], appearance cues [START_REF] Tan | Color model-based realtime learning for road following[END_REF] or the 3D aspect by using prior knowledge about the environment as an extra source of information [START_REF] Coulombeau | Vehicle yaw, pitch, roll and 3d lane shape recovery by vision[END_REF]. The detection methods using lane marking approaches may fail in unmarked roads, and some approaches overcome this assumption discriminating the overall road area with some appearance cues such as color feature [START_REF] Tan | Color model-based realtime learning for road following[END_REF][9] [START_REF] Alvarez | Road detection based on illuminant invariance[END_REF], and texture feature [START_REF] Rasmussen | Grouping dominant orientations for ill-structured road following[END_REF] [START_REF] Kong | General road detection from a single image[END_REF]. Outcomes from the color feature may present poor results when there is high intra-class variability presents in the dynamic nature of the scenes. Texture is scale-dependent and is affected by the strong perspective in road image. Detection using prior knowledge of 3D aspect of the road may fail due to the car's dynamic and road's imperfections. In the case of stereo vision, the 3D information of the environment is typically used to estimate free spaces and obstacles, with specific techniques like the V-Disparity Map [START_REF] Labayrade | Real time obstacle detection in stereovision on non flat road geometry through "V-disparity" representation[END_REF] [START_REF] Broggi | Obstacle detection with stereo vision for off-road vehicle navigation[END_REF]. However , the detection of road in urban environments must deal with different sources of noise that makes it difficult to define the disparity map. Some approaches propose improvements to the model by merging color, texture and adding 3D information [15][16].

This work proposes a novel method for road detection in inner-city, using a set of probabilistic distribution to model the classifier of a Joint Boosting algorithm. Differently from others works, this approach creates a different set of features merging a technique called Diston, proposed by previous works (3D information) with the Texton (2D texture and color) to compute a set of probabilistic distribution for each superpixel. The probabilistic distribution feature's descriptor is used to model the weak classifier used in the Joint Boosting algorithm.

The rest of the paper is organized as follows: Section II presents the Image Segmentation, Texton Mapping and Dispton Mapping processes. Section III presents the formalism to model a set of probabilistic distribution using a Joint Boosting algorithm. Section IV presents the results of an experiment using the KITTI benchmark. Finally, Section V presents some conclusions about what we have learned from the study and proposals for future work.

II. IMAGE PROCESSING

In this section is presented a description of three parallels processes namely Image Segmentation, Texton Map and Dispton Map. As can be seen in Figure 1, the resulting output of these processes will be used as a source for generating the set of probability distribution in a second step.

A. Image Segmentation

Among various approaches used to produce superpixels, the most used nowadays in literature is the mean-shift algorithm [17][18]. In this work however, it is explored another methodology based on Watershed Transform that since [START_REF] Beucher | Road segmentation and obstacle detection by a fast watershed transformation[END_REF] has been applied to road detection. The combination of Watershed Transform with other filters has presented encouraging results as shown in [START_REF] Vitor | A 2d/3d vision based approach applied to road detection in urban environments[END_REF].

In order to obtain a reasonable flexibility to determine the segmentation level, we have used the same approach described in [START_REF] Vitor | A 2d/3d vision based approach applied to road detection in urban environments[END_REF]. Three pre-filters were added: (i) the Morphological Gradient Adjusted (M G adj ), (ii) the AreaClose and (iii) the Hmin. Filter (i) is applied to obtain the high frequency image, where an adjustment is done to improve the low-contrast of high frequency in shadow areas [START_REF] Lima | A disparity map refinement to enhance weakly-textured urban environment data[END_REF]. The formal definiton can be seen in ( 1):

M G Adj = c [(f ⊕ g e ) -(f g i )] γ , if {∀x|f (x) < ρ} (f ⊕ g e ) -(f g i ) , otherwise (1)
In equation ( 1) f is the image function, g e and g i are structuring elements centered at the origin, the operators ⊕ and are respectively dilation and erosion. The nonlinear transformation at low-contrast of high frequency has the factor γ setted to 0.45 and ρ is the threshold. The constant of normalization c is defined by the equation [START_REF] Thorpe | Vision and navigation for the carnegie-mellon navlab[END_REF].

c = max((f ⊕ g e ) -(f g i )) max((f ⊕ g e ) -(f g i )) γ (2)
The goal of the filters (ii) and (iii) is to control the segmentation level of the Watershed Transform by acting on the regional minimum of GM Adj . The procedure is identical to [START_REF] Vitor | A 2d/3d vision based approach applied to road detection in urban environments[END_REF], where the parameter λ of AreaClose determines the area of the regional minimum to be cut out, and the parameter h of Hmin determines the height from the regional minimum to be also cut out. The final outcome applied in a sample image can be observed in Figure 2. Notice that the classification's procedure is performed making a probability distribution for each super-pixel. Therefore, the analysis of these parameters is done to understand their sensibility in the final result of the segmentation.

B. Texton Map

In the last decade, Textons have been proven effective for generic feature representation of object [START_REF] Ladicky | Associative hierarchical crfs for object class image segmentation[END_REF] [START_REF] Krähenbühl | Efficient inference in fully connected crfs with gaussian edge potentials[END_REF], where a class demands different appearances to have a compact representation maintaining their efficiency. Thereby, the methodology applied for this block is to learn a dictionary of Textons using a textonization technique [START_REF] Shotton | Textonboost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context[END_REF], which allows to perform a dense-texture-based feature extraction for all pixels. The process of textonization generates the Texton Map, having the same size of the image. The Textons contained in the dictionary have their value associated with all pixels in this map. It can be seen as a pre-classification or a transformation from feature's space to the texton's space. Thus, this process is done by applying the K-Means algorithm on a feature's space. Denoting a dictionary as D, each texton's element x j ∈ D = {x 1 , x 2 , ..., x K } represents a cluster generated by the algorithm, employing the Euclidian-distance as a metric. Finally, it is obtained the Texton Map T ∈ N 2 with the pixel i having value x j ∈ D.

In this work, the textonization executed in various feature's spaces, as it can be seen in [START_REF] Ladicky | Associative hierarchical crfs for object class image segmentation[END_REF]. The set includes 17-dimensional filter bank, 3-dimensional CIELAB color, 81-dimensional histograms of oriented gradient [START_REF] Dalal | Histograms of oriented gradients for human detection[END_REF] and 2-dimensional normalized pixel location. All feature's descriptor are whitened (to give zero mean and unit covariance) to learn the dictionaries of textons in which their configuration were assigned to D b = 400 clusters, D c = 128 clusters,D g = 150 clusters and D l = 144 clusters, respectively. The output result for this module can be seen in Figure 3.

C. Dispton Map

Based on the approach explained in section II-B, Texton Maps are able to discriminate between class of similar textures. However this technique lacks spacial information. This section presents an approach to build two additional dictionaries over 3D information from Stereo Vision. This method is called Dispton Map and it aims at creating meaningful clusters based on the Disparity Map, denoted by I ∆ . Attempting to have the same functional advantage provided by the usage of U-Disparity and V-Disparity algorithm to filter and extract the navigable area and obstacles in literature [START_REF] Labayrade | Real time obstacle detection in stereovision on non flat road geometry through "V-disparity" representation[END_REF] [START_REF] Soquet | Free Space Estimation for Autonomous Navigation[END_REF], this work addresses another way to embed these information in a dictionary of Dispton, generating the Dispton Maps from I ∆ .

Firstly, the technique consists in putting in evidence the peaks of the U-V Disparity maps, which concentrates the relevant information to start the process of Disptonization. Defining U-Disparity as I u ∆ and V-Disparity as I v ∆, they are obtained from a histogram for each column, I u ∆ = {hist(I∆(:, u))|∀u ∈ {0..width -1}} and for each row, I v ∆ = {hist(I∆(v, :))|∀v ∈ {0..height -1}}. Like Watershed Transform, they can be seen as surfaces to apply the Hmin and filter the regional minimum (in this case considered as noise) of these surfaces. The result maps, denoted I h u ∆ and I h v ∆ are calculated by the binarization. A Hough Transform is executed to detect line segments, characterized by l u and l v .

After that, to build the dictionary of U-Dispton (D u ) is applied the clusterization where the points from each line segment l u j , supplies seeds to perform the clusterization of dispton's element j ∈ {1..N umberOf Lines}. In equation (3), the clusterization process of a line segment l u j , denoted by Λ(I h u ∆)(l u j ), is given by:

Λ(I h η ∆)(l η j ) = C η j , if {x} ∈ N x (I h η ∆) = 0 0 , otherwise (3)
Where the cluster C η j is defined in equation ( 4). Note that the variable (η) can be either u or v.

C η j = {j|x 1 W + x 2 ∈ {l η j } ⊂ N x (I h η ∆)} (4)
Here, W is the number of image columns, and x 1 and x 2 are the row and column coordinates respectively, and the term N x (.) represents the neighbors of the x element. There are two additional clusters given by

C u 1 = {I u ∆(v, :)|v ∈ {0, 1, .., τ }} and C u 2 = (I u ∆ ∩ C u 1 )
, where ( ) represents its complement. Therefore, the D u is given by the union of all clusters (5):

D u = { ∀j∈l u Λ(I h u ∆)(l u j ) ∪ C u 1 ∪ C u 2 } (5)
Finally, with the U-Dispton dictionary, it is possible to obtain the U-Dispton map (Dm u ) by the following equation [START_REF] Felisa | Robust monocular lane detection in urban environments[END_REF]:

Dm η = {D η (I∆(x))|∀x; I∆(x) = 0} (6)
In order to build the V-Dispton dictionary (D v ), the clusters are created separately. Following Hu and Uchimura [START_REF] Hu | U-v-disparity: an efficient algorithm for stereo vision based scene analysis[END_REF], a road is modeled as a plane so that it can be represented by straight slope line segments in the V-disparity map. In this sense, the goal of the first cluster is to curve fitting these line segments to represent the surface of navigable area. From l v set, a subset l vs is obtained filtering out the line segments with a given vertical orientation [START_REF] Tan | Color model-based realtime learning for road following[END_REF]:

l vs = {l v i |∀l v i ; Ang(l v i ) < 90°-ψ} (7)
The Ang(.) represents the angle of inclination with the reference defined on the bottom-left image and ψ a parameter of input. Thus, the road surface can be formed by a succession of plane's parts, being projected as a piecewise linear curve [START_REF] Hautiere | Road scene analysis by stereovision: a robust and quasi-dense approach[END_REF]. In order to connect the line segments that represent the surface, the algorithm sorts the l vs set based on the distance from the line segment to the reference.

Starting from the first line segment l vs 0 to the last one l vs n , the constraints that define whether two line segments can be connected, are given by the follow equation ( 8), which l vc is the set of connected line segments.

l vc = min(dist(l vs i , l vs j )) ∀l vs j ∈ {l vs > l vs i } and if l vs j ⊂ AreaSupport(l s- i , l s+ i ) (8)
Where the function dist(.) between two line segments is calculated considering the Euclidian distance from the nearest points of the current line segments, limited by a given maximum distance ε between them. The function AreaSupport(.) delimits the search area by two line segments as seen in [START_REF] Alvarez | Learning photometric invariance from diversified color model ensembles[END_REF].

AreaSupport(l s1 , l s2 ) = 1 , if right(l, l s1 ) and lef t(l, l s2 ) 0 , otherwise

This area is defined by a translation from l vs i given by the σ parameter, then l s- i = l vs i -σ and l s+ i = l vs i + σ. The other two functions in this equation return true case when the line segment is on right and left of the reference lines. As a result, the cluster C v 1 is obtained applying the equation (3) on the l vc set (Λ(I h v ∆)(l vc j )) with one more constraint, where all pixels cannot cross out the line (l lim1 ) formed by the first and last points of l vc (added a small shift constraint). In addiction, the second cluster is generated taking those pixels which cross out the first one and is restricted to another shifted line l lim2 = l lim1 + σ 2 , resulting the equation ( 10):

C v 2 = {Λ(I h v ∆)(l vc j )| if AreaSupport(l lim1 , l lim2 )} (10)
To finish the Disptonization, the last two clusters are generated by C v 3 = {I v ∆(:, u)|∀u ∈ {1, .., τ }}, where τ defines the max disparity to be considered as background or infinity, and

C v 4 = {(I v ∆∩(C v 1 ∪C v 2 ∪C v 3
)) }. The V-Dispton dictionary (D v ) is defined as:

D v = {C v 1 ∪ C v 2 ∪ C v 3 ∪ C v 4 } (11)
And the generation of V-Diston map is obtained by the equation [START_REF] Felisa | Robust monocular lane detection in urban environments[END_REF]. Algorithm 1 summarizes the Disptonization process. for l u and l v ; 4: Determine the U-Dispton dictionary D u by eq. ( 5):

-Apply the clusterization on l u , eq. ( 3); 5: Determine the V-Dispton dictionary D v by eq. ( 11):

-Filter out the vertical lines to take l vs , eq. ( 7); -Find out the connected lines l vc , eq. ( 8); -Define the clusterization to C v 1 , C v 2 , C v 3 , C v 4 ; 6: Generate the UV-Dispton map by eq. ( 6) Fig. 4. The Dispton maps obtained from the disptonization process using the Disparity Map.

Note that the V-Dispton map has 4-dimensional clusters and the U-Dispton map has N-dimensional clusters. Intuitively, they aim at storing important information such as navigable area, sidewalk, obstacles and background. The Ndimensional structure from U-Dispton map dynamically retrieve the representation of all possible different obstacles in the scene, as can be seen in Figure 4. With the Texton maps and Dispton maps, the next section explains how they are combined with the superpixel to perform the classification.

III. ROAD RECOGNITION

This section presents an approach to represent and compute the classification of the road class, as shown in previous sections, where the road recognition can be executed using an adapted version of the Joint Boosting algorithm [START_REF] Torralba | Sharing features: efficient boosting procedures for multiclass object detection[END_REF]. In fact, the algorithm is inspired by the TextonBoost approach [START_REF] Shotton | Textonboost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context[END_REF], which iteratively builds a strong classifier as a sum of week classifiers, simultaneously selecting discriminative features. We have improved the representation of weak classifiers using a specific shape filter. Thus, the novelty is to build a set of probability distribution of the Texton and Dispton maps from the decomposition of the scene into a number of semantically consistent regions, supplied by the segmentation result shown in section II-A, to model the weak classifier.

The process could be formally explained taking into account the maps {M f : f ∈ {F }} where F = {b, c, g, l, v, u} is the set of Textons and Disptons. Each element i in the map M f ∈ N 2 belongs to exactly one region, identified by its region-correspondence variable S r ∈ {1, ..., N umSegments}. The r-th region is then simply the set of elements i r whose region-correspondence variable equals r, i.e., i r = {i :

M f i = r}. We use X f i = {X f 1 , X f 2 , .
.., X f N } to denote the set of random variables corresponding to the f -th value of i-th element into M f . Any possible assignment to the random variables X f i = x f j takes values from j ∈ D f , which D f is defined by the constructed dictionary for each f ∈ F generated in the sections II-B and II-C, .

The probability of the X f i if given by P (X f i = x f j ), and the associated set of probability distribution under the S r is denoted by P (X r ), as can be seen in the equation ( 12):

P (X r ) = { f ∈F 1 Z ir P (x f j ) |∀j ∈ D f } (12)
In Equation ( 12), Z is a normalization factor for each probability distribution set. Using the probability representation of Textons and Disptons, the weak classifiers are modeled as comparisons of this probability distribution to a decision stump based on a threshold, where each weak classifier is shared between a set of classes, allowing a single probability to help classify several classes at once. They are defined by wc containing 2-tuples [x rand , P (x random )], where the first component represents a random possible assignment {x rand : x f j ∈ D f } and its value of probability randomly defined. To express how well the probability distribution of P (X r ) at a given x f j matches the weak classifier, a comparison response is given by equation ( 13):

d(wc, S r ) = 1 -[P (x rand ) -P (X r = x f j )] 2 (13)
Thereby, the Joint Boosting algorithm is an additive model of the form H(c l ) = M m=1 h m (c l ), that sum the classification confidence of M joint weak classifiers. In this case, H(c l ) represents the strong learned classifier and the weak classifiers are extended to discriminate the share between classes. Therefore, each weak-learner is modeled as a decision stump of the form:

h(c l ) = aδ(d(wc, S r ) > θ) + b , if {c l ∈ L} κ c l , otherwise (14)
Where δ(.) is a 0-1 indicator function. The share is given by those classes (c l ∈ L), where the weak learner gives h(c l ) ∈ {a+b, b} depending on the comparison of d(wc, S r ) to a threshold θ. The constant k c l ensures asymmetrical sets of positive and negative training examples for those classes that do not share the feature (c l / ∈ L). Thus, the resulting classification output is defined by the probability conversion given by [START_REF] Soquet | Road segmentation supervised by an extended v-disparity algorithm for autonomous navigation[END_REF]:

P = 1 Z exp -H(c l ) (15)
Here, the Z represents the normalization factor into the classes c l ∈ L.

IV. EXPERIMENTAL RESULTS

In this subsection we present the results of our experiments using real driving situations. It is used the Urban Kitti-road dataset 1 , which consists of 600 frames (375x1242 px) recording from five different days and containing relatively low traffic density [START_REF] Fritsch | A new performance measure and evaluation benchmark for road detection algorithms[END_REF]. The data are categorized in three sets having each one a subset of training and test images, representing a typical road scene in inner-city. The first set called the UU is formed by images taken from an urban unmarked area and has 98 images for training and 100 images for testing. The second set called the UM is formed by images taken from an urban marked two-way road and has 95 images for training and 96 images for testing. Finally, the final set called UMM is formed by images taken from an urban marked multi-lane road and has 96 images for training and 94 images for testing. The experiments in this work use the training set in the perspective space to learn the classifier and the metric space to calculate the complete evaluation of this approach. The evaluation process is done on the metric space in order to capture the fact that vehicle control happens in the 2D environment. Further, the evaluation in perspective space is biased by the fact that the pixel's value in near range is more homogenous and covers a larger area of the evaluated perspective pixels [START_REF] Fritsch | A new performance measure and evaluation benchmark for road detection algorithms[END_REF].

The learning process were executed separately for each category. Thus, a sample set was built for each one, extracting 12.8E + 4 samples from the UU image training set, 12.6E + 4 samples from the UM image training set and 11.7E + 4 samples from the UMM image training set. Table I shows the results of the quantitative evaluation of our approach applied in the test set. For comparison purposes, it also presents the baseline provided as a lower bound, by averaging all ground truth road maps from the present testing set, and also the results of the proposed road detection using Artificial Neural Network (ANN) [START_REF] Vitor | A 2d/3d vision based approach applied to road detection in urban environments[END_REF]. As can be seen for the UU category, our approach reached an improvement of 26.12% if compared to ANN and 10.80% to Baseline, using the testing set on the metric space. With respect to the UM category, our approach reached a level of correctness of 87.60%, reaching a gain of 24.96% compared to ANN and overcoming the baseline approach in 5.07%. Using the UMM category, which is less complex if compared to others, we can highlight our approach, overcoming the ANN approach with 9.03% and 13.95% compared to the baseline. The qualitative result for this challenging dataset can be seen in Figure 5, presenting a classified image using the perspective space. The same images using the metric space can be seen in Figure 6.

To conclude the evaluation process, Table II presents the final results merging all categories. Our approach presents 87.21% of correctness for challenging urban Kitti-road benchmark. According to our experiments we believe that our approach improved the detection of roads in scenarios with high complexity, significantly outperforming the baseline.

V. CONCLUSIONS AND FUTURE WORKS

In this paper, we have proposed an approach for road recognition for inner-city based on appearance, shape and spacial model learned from training data. The main contribution of this work is the creation of a probabilistic distribution based on Texton and Dispton maps to model weak classifiers used in a Joint Boosting classifier.

Experiments conducted on real driving situations demonstrate the qualitative and quantitative evaluation of our algorithm to detect road despite the presence of shadows and other objects in the scene, inherent from the complexity of inner-city environments. The result also provides the benefits of our approach over existing methods.

We are still working on improvements to reduce the processing time using the GPU architecture, and also working Fig. 6. The resulting output of our approach in metric space (highlighted in green). The first, second and third columns show the UU, UM and UMM category image respectively, from the images of Figure 5.

on Self-Organizing Maps [START_REF] Kohonen | Self-organizing maps[END_REF], in order to better discriminate the road pattern and extending the recognition for different classes such as vehicles, builds, sidewalks, etc. This approach could improve the prediction of the classifier. The complete application will be embedded in a real carlike robot, sponsored by the project ROBOTEX, from the Heudiasyc laboratory, to perform autonomous driving in urban environments.

Fig. 1 .

 1 Fig. 1. Solution block diagram.

Fig. 2 .

 2 Fig. 2. Example of an influence surface for the parameters λ and h on the number of segments in an given image. In (a) λ 1 = 5, h 1 = 2 and 4090 segments; (b) λ 2 = 30, h 2 = 5 and 427 segments; (c) λ 3 = 80, h 3 = 15 and 73 segments.

Fig. 3 .

 3 Fig. 3. The texton maps resulting from the textonization process using different features.

Algorithm 1

 1 Disptonization algorithm: 1: Process I u ∆ and I v ∆ from I∆; 2: Apply the Hmin filter on I u ∆ and I v ∆; 3: Binarize and obtain the line segments by Hough Transf.

Fig. 5 .

 5 Fig. 5. The resulting output of our approach in perspective space. The first, second and third rows show the UU, UM and UMM category image respectively.

TABLE I RESULTS

 I [%] OF PIXEL-BASED FOR THE ALL CATEGORIES ON THE METRIC SPACE EVALUATION.

			Urban Unmarked (UU)
		Fmax	AP	Prec.	Recall	FPR	FNR
	Baseline [30]	69.49	73.84	65.73	73.70	12.78	26.30
	ANN [20]	54.17	36.86	39.50	86.19	43.92	13.81
	Our	80.29	69.05	85.58	75.61	4.24	24.39
			Urban Marked (UM)
		Fmax	AP	Prec.	Recall	FPR	FNR
	Baseline [30]	82.53	85.59	79.24	86.11	10.41	13.89
	ANN [20]	62.64	46.80	50.18	83.34	38.21	16.66
	Our	87.60	76.04	85.92	89.36	6.76	10.64
		Urban Marked Multi-Lane (UMM)
		Fmax	AP	Prec.	Recall	FPR	FNR
	Baseline [30]	76.17	78.42	65.02	91.95	57.89	8.05
	ANN [20]	81.09	68.93	70.43	95.56	46.94	4.44
	Our	90.12	85.04	88.15	92.12	14.50	7.82

1 http://www.cvlibs.net/datasets/kitti/eval_road. php

TABLE II RESULTS

 II [%] OF PIXEL-BASED FOR COMPLETE URBAN ROAD AREA EVALUATION.

		Fmax	AP	Prec.	Recall	FPR	FNR
	Baseline [30]	75.61	79.72	68.93	83.73	21.73	16.27
	ANN [20]	68.12	51.52	54.85	89.85	42.59	10.15
	Our	87.21	77.79	86.96	87.47	7.55	12.53


Giovani B Vitor

Alessandro C Victorino

Janito V Ferreira

Comprehensive performance analysis of road detection algorithms using the common urban Kitti-road benchmark

Keywords: Road Recognition, Computer Vision, Artificial Neural Network, Joint Boosting, Texton Map, Dispton Map, Watershed Transform

The navigation of an autonomous vehicle is a highly complex task and the dynamic environment is used as a source for reasoning. Road detection is a major issue in autonomous systems and advanced driving assistance systems applied for inner-city. Uncertainty may arise in environments with unmarked or weakly marked roads or poor lightning conditions. Moreover, when a common benchmark is not used, it is hard to decide which approach performs better on the road detection problem. This paper introduces a comprehensive performance analysis of two road recognition approaches using the urban Kitti-road benchmark. The first approach makes the extraction of a feature set based on statistical measures of 2D and 3D information from each superpixel. An Artificial Neural Network is used to detect the road pattern. The second approach extracts the feature set based on a multi-normalized histogram of Textons and Disptons for each superpixel. This feature set is used as a source for a Joint Boosting algorithm to model the road pattern. The proposed work presents a detailed evaluation highliting the pros and cons of each approach.

I. INTRODUCTION

In recent years, several applications for control of autonomous vehicles and specialized Advanced Driving Assistance Systems (ADAS) were proposed [START_REF] Karasev | Depth invariant visual servoing[END_REF] [START_REF] Rawashdeh | Mult-sensor input path planning for an autonomous ground vehicle[END_REF]. In most cases machine vision is used as a main source of information for road detection thanks to the facility to extract measurements related to texture and color. Furthermore, several vehicles are already equipped with cameras and this large-scale adoption could be motivated by their low cost. Specifically for ADAS systems, machine vision has been used to detect road surfaces where lane keeping assistance systems have already been embedded with a limited capability to detect roads with smooth curvatures [START_REF] Khnl | Monocular road segmentation using slow feature analysis[END_REF].

Several approaches related to vision-based road detection have been proposed, varying from mono to stereo vision sources. They aim at solving several challenging problems such as the continuously changing backgrounds in different environments (inner-city, highway, off-road), different road types (shape and color), the presence of different objects (signs, vehicles, pedestrian) and also different imaging conditions (variation of illumination and weather conditions).

In order to provide support on unmarked roads that are common in inner-city environments, general approaches overcome the assumption of lane marking discriminating Authors are members of 1 Heudiasyc UMR CNRS 7253 Université de Technologie de Compiègne, 2 Universidade Estadual de Campinas (UNI-CAMP). Giovani B. Vitor holds a Ph.D. scholarship from CNPQ. Contact authors giovani.bernardes-vitor@hds.utc.fr the overall road area with some appearance cues, spacial information or the combination of both. Several works emphasize the color feature in their models [START_REF] Tan | Color model-based realtime learning for road following[END_REF][5] [6], while others use the texture feature [START_REF] Rasmussen | Grouping dominant orientations for ill-structured road following[END_REF] [START_REF] Kong | General road detection from a single image[END_REF]. Outcomes from the color feature may present poor results when there is high intra-class variability presents in the dynamic nature of the scenes. Texture is scale-dependent and is affected by the strong perspective in road image. Some approaches propose improvements in the model by merging color, texture and adding 3D information [START_REF] Soquet | Road segmentation supervised by an extended v-disparity algorithm for autonomous navigation[END_REF][10] [START_REF] Vitor | A 2d/3d vision based approach applied to road detection in urban environments[END_REF].

All presented approaches have been evaluated using different datasets and criterias. Therefore a fair performance comparison is prohibitive [START_REF] Fritsch | A new performance measure and evaluation benchmark for road detection algorithms[END_REF]. Following the approach proposed by Fritsch et al. [START_REF] Fritsch | A new performance measure and evaluation benchmark for road detection algorithms[END_REF], this paper contributes to produce a standard performance analysis using the challenging urban KITTI-road benchmark as a dataset. This work proposes an evaluation of two distinct approaches highlighting the pros and cons of each one. The first approach makes the extraction of a feature set based on statistical measures of 2D and 3D information for each superpixel. An Artificial Neural Network is used to detect the road pattern. The second approach extracts the feature set based in a multi-normalized histogram of Textons and Disptons for each superpixel. This feature set is used as a source for a Joint Boosting algorithm to model the road pattern.

The rest of the paper is organized as follows: Section II presents the Artificial Neural Network rendering the 2D image segmentation and 3D image processing to model the road pattern. Section III presents the multi-normalized histogram with Joint Boost algorithm, covering the Texton maps and the Dispton maps. Section IV presents the results of the two approaches using the space and metrics perspective. Some conclusions presenting what we have learned from the study and proposing future work are presented in Section V.

II. ARTIFICIAL NEURAL NETWORK FOR ROAD

RECOGNITION

This section describes the first algorithm for road recognition. The algorithm executes the 2D image segmentation and 3D image processing to compose a feature's descriptor based on the texture information and spacial information. This feature's descriptor is used by the Artificial Neural Network (ANN) to learn the road pattern.

A. 2D Image Processing

The 2D Image Segmentation algorithm performs a combination of filters with Watershed Transform to get the superpixel. This segmentation process uses an approach inspired by the techniques used by the domain of morphological image processing [START_REF] Dougherty | Hands-on Morphological Image Processing[END_REF], where three morphological filters are applied to reach the superpixel, namely Morphological Gradient Adjusted, the AreaClose and the Hmin. The first one is applied to obtain the highest frequency of the image, where an adjustment is made to improve the low-contrast of high frequency in shadow areas [START_REF] Lima | A dis-parity map refinement to enhance weakly-textured urban environment data[END_REF]. The AreaClose filter is employed to filter out connected components smaller than a given parameter λ, followed by the Hmin filter that is responsible for eliminating the local minimum given a second parameter h. The main goal of these filters is to give flexibility allowing the Watershed Transform to determine the segmentation result, i.e, ranging the parameter λ and h, it is possible to define a fine or a coarse segmentation result. This process follows the same approach proposed in [START_REF] Vitor | A 2d/3d vision based approach applied to road detection in urban environments[END_REF] and its result can be seen in Figure 1a.

B. 3D Image Processing

The 3D Image Processing algorithm uses the rectified pair of stereo images to allow an easy pre-classification for the image data into drivable and non-drivable areas. The approach takes advantage of the Epipolar geometry [START_REF] Faugeras | Three-dimensional computer vision: A geometric view point[END_REF]. First, the disparity map (I ) of the stereo pair is built using the Sum of Absolute Differences (SAD) correlation algorithm. As described in [START_REF] Labayrade | Real time obstacle detection in stereovision on non flat road geometry through "V-disparity" representation[END_REF], the world environment can be approximated by horizontal and vertical planes related to the camera. Based on this assumption, each disparity map (I ) are then projected in the v-disparity map (I v ) [START_REF] Soquet | Road segmentation supervised by an extended v-disparity algorithm for autonomous navigation[END_REF], where each line on I v represent these planes. The next step is the execution of the Hough Transform with an auxiliary algorithm to detect the lines associated with the drivable area. In I v , the continuous slope lines represent the drivable area, while the non-drivable areas are approximately represented by vertical lines. As a result, the lines from Hough transform are filtered out considering a given parameter of inclination performed by the auxiliary algorithm. It applies also the moving average technique along the time, to avoid the noisy data in the I v derived from a wrong correlation in I . Finally, the detected lines are reprojected into the image, as can be seen in Figure 1b, which generates an estimation of drivable and non-drivable area.

C. Artificial Neural Network algorithm

The Artificial Neural Network (ANN) using a Multilayer Perceptron (MLP) is applied to learn the road pattern resulting in the final classification of the road recognition. First, the method constructs a feature's descriptor based on the superpixel generated from the 2D image segmentation. 3D and 2D information are generated for each superpixel. The first set of features consider the percentage of the drivable and non-drivable areas where it is based on the intersection of the resulting pre-classification of 3D image processing with the respective superpixel. The second set of features is based on statistical measures, as proposed by [START_REF] Shinzato | A road following approach using artificial neural networks combinations[END_REF], like mean, probability, entropy and variance. These values were calculated by the RGB1 and HSV2 color space values of each superpixel.

The network training is based on the Backpropagation technique and its structure was projected with three layers, namely input, output and hidden layers. The size of the input layer corresponds to the number of features extracted which was defined in the experiments with six neurons in its final version. The hidden layer was defined with 30 neurons, where all neurons use the sigmoid activation function. Finally, the output layer has two neurons to classify the feature's descriptor as road surface or non-road surface, which response vary from 0 to 1. Figure 1 presents all processes performed by this first approach.

III. MULTI-HISTOGRAM-BASED JOINT BOOSTING FOR ROAD RECOGNITION

In this section is given a brief understanding of the second algorithm for road recognition. The method merges 2D and 3D information, resulting in a multi-normalized histogram that model the appearance, shape and context using a Joint Boost algorithm. Differently from the first one, this approach creates a form to represent the 3D information. It encapsulates the 3D information on Disptons3 to compose jointly with the Textons4 , a different set of features, to better represent the road class given by the complex environment.

A discriminative model for road class is learned based in the multi-normalized histogram performed on superpixel, shown in section II-A, and classified using a Joint Boosting classifier.

A. Texton Maps

The Textons have been proven to be effective to discriminate between some classes of similar textures [START_REF] Ladicky | Associative hierarchical crfs for object class image segmentation[END_REF]. The method applies the textonization process based on the extraction of various features to get the texton maps [START_REF] Shotton | Textonboost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context[END_REF]. This process of textonization construct a dictionary of Textons by using an unsupervised classifier. The unsupervised classifier is given by the K-Means algorithm where the Dictionary of Textons t i ∈ D = {t 1 , .., t K } is defined by clusters generated according to the given feature. At the end, a particular texton t i is associated to each pixel, providing a mapping of Textons T ∈ N 2 denoted by texton map.

The textonization is performed on a set of feature's descriptor, as can be seen in [START_REF] Ladicky | Associative hierarchical crfs for object class image segmentation[END_REF]. The set include 17dimensional filter bank, 3-dimensional CIELAB color, 81dimensional histograms of oriented gradient [START_REF] Dalal | Histograms of oriented gradients for human detection[END_REF] and 2dimensional normalized pixel location. All feature's descriptor are whitened (to give zero mean and unit covariance) to learn the dictionaries of textons in which their configuration were setted to 400 clusters, 128 clusters, 150 clusters and 144 clusters, respectively. The disptonization process generates two features called V-Dispton map and U-Dispton Map, containing 4 clusters and N clusters respectively. The dispton maps can be seen in Figure 2.

B. Dispton Maps

C. Multi-Normalized-Histogram Joint Boosting algorithm

The multi-normalized-histogram joint boosting algorithm abbreviated by HistonBoost, is built to model the road class, using an adapted version of the joint boosting algorithm [START_REF] Torralba | Sharing features: efficient boosting procedures for multiclass object detection[END_REF]. This algorithm is derived from TextonBoost [START_REF] Shotton | Textonboost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context[END_REF], which iteratively builds a strong classifier as a sum of week

h(c i ) = aδ(d(rh, sp i ) > θ) + b , if c i = 1 κ c i , otherwise (1)
In equation ( 1), rh represents the random histogram comparator and sp i represents the multi-normalized histogram of the superpixel i. The d(.) is the χ 2 function and δ(.) is a 0-1 indicator function. The weak learner gives h(c i ) ∈ {a + b, b} depending on the comparison of d(rh, sp) with a threshold θ. The constant k ci ensures asymmetrical values of positive and negative training examples. The result is the probability given by (2):

P = 1 Z exp -H(ci) (2)
where Z is a normalization factor.

IV. EXPERIMENTAL RESULTS

In this section we present the results of our experiments, using the Urban Kitti-Road 5 dataset. The validation platform were implemented in C++, and the experiments were executed in one equipment with an Intel Xeon E5-1650 processor with 3.20Ghz and with 16Gb DDR3, running the version 7 of the Windows OS.

In brief, this dataset consists of 600 frames (375x1242 px) recording from five different days and containing relatively low traffic density [START_REF] Fritsch | A new performance measure and evaluation benchmark for road detection algorithms[END_REF], representing a typical road scene in inner-city. Data is categorized in three sets having each one a subset of training images and a subset of test images exclusively for evaluation performance. The first one is urban unmarked (UU) with 98 images for training and 100 images for test, followed by urban marked two-way road (UM) with 95 and 96 images, and urban marked multi-lane road (UMM) with 96 and 94 images. The experiments in this work were based in the perspective and metric space for all categories, which each one had its own learning process. Thus, a sample set was built for each one, extracting 12.8E + 4 samples from the UU image training set, 12.6E + 4 samples from the UM image training set and 11.7E + 4 samples from the UMM image training set. These sets were used in both algorithms to perform the feature's descriptor using the ANN approach and the multinormalized histogram for the Joint Boosting approach, all of them using the superpixel area.

The methodology to train the ANN algorithm uses the cross validation method, splitting the training images in training, validation and test subsets, containing 15% , 7% and 78% respectively. The time to train each category takes around 24 hours and the classification time for a single image takes around 3 seconds. It should be mentioned that some adaptations from the first version proposed in [START_REF] Vitor | A 2d/3d vision based approach applied to road detection in urban environments[END_REF] were required: (i) it does not use the moving average technique, because it is applicable only for image sequences, (ii) the learning process does not use the strategy of training with subclass (shadow area, normal area and land marks), where it would improve the final result of road detection. In the case of the learning process to Joint Boosting algorithm, the training images were splitted into training subset containing 40%, and the test subset with 60%. The time to train each category of this approach takes around 72 hours and the classification time for a single image takes around 2.5 minutes.

For all quantitative evaluation, the baseline to road area is provided as a lower bound [START_REF] Fritsch | A new performance measure and evaluation benchmark for road detection algorithms[END_REF], by averaging all ground truth road maps from the present validation set. The Table I presents the quantitative evaluation for the UU category. As can be seen, the HistonBoost approach reached a improvement of 24.28% from ANN and 4.13% from Baseline in the perspective space. Based on the metric space using the testing set, the same approach takes 73.51%, reaching a gain of 19.34% and 4, 02% from ANN and Baseline. The 5 http://www.cvlibs.net/datasets/kitti/eval_road. php qualitative result for this challenging category can be seen in the Figure 3, where it presents the classification process on perspective space using the training set. With respect to UM category, Table II shows that Histon-Boost reached a improvement of 19.04% from ANN and from Baseline this percentage is approximately 1.00% in the perspective space. In case of the metric space using the testing set, this approach reached 83.71%, reaching a gain of 21.07% and 1, 18% from ANN and Baseline. The qualitative result for this category can be seen in the Figure 4. Using the UMM category, which is the less complex if compared to others, we can highlight the ANN approach due to its response in the testing set overcoming the baseline approach with 4.92%. The result for this category can be seen in Figure 5. Compared to other approaches, ANN outcomes presents the worst results. It can be explained by the higher complexity of the Kitti-road dataset, or, the training process used for ANN was not adequate, having low expressiveness if observed the strategy of subclasses used in [START_REF] Vitor | A 2d/3d vision based approach applied to road detection in urban environments[END_REF]. To conclude the evaluation process, Table IV presents the final results merging all categories. The HistonBoost approach presents 83.41% of this challenging urban Kittiroad benchmark, where can be considerable a satisfactory result given the complexity of the scenarios. Figure 6 shows the qualitative results using the testing set and demonstrating the performance in the metric space, denoted by Bird's Eye View (BEV). The road detection results for HistonBoost and ANN approaches are demonstrated in red.

V. CONCLUSIONS AND FUTURE WORKS

This proposed work presents the meaningful performance analysis using two distinct road detect algorithms in inner- city, showing the pros and cons of each approach. The first approach constructs a feature's descriptor extracting statistical measures of 2D and 3D information to be classified using an Artificial Neural Network. The second approach construct a multi-normalized histogram feature's descriptor to be recognized by a Joint Boosting classifier.

Based on the experimental results, the performance analysis demonstrates that the HistonBoost approach copes better than the ANN approach for all dataset in the perspective and metric space. The main advantages of this method are: (i) The union of Textons and Disptons to compose the feature's descriptor shows promising applicability; (i) The Joint Boosting algorithm is able to build a satisfactory discriminative model to road pattern in complex scenarios; (iii) The Module independency of this approach leverages the usage of parallelization and improvement on resources usage. However, the processing time is still an issue, if compared with other approaches.

In general, The high level of complexity to build and test a recognition model is due to the different characteristics present in inner-city environments. We believe that an alternative to model a complex road pattern should take into account the learning of intra-classes pattern derived from the main class such as normal area, shadow area, horizontal land mark if exist, area with influence of sun, etc. Thereby, an strategy using any kind of unsupervised classifier to leave arise clusters of these intra-classes automatically, and after that, merging this result process in a supervised method could be interesting to improve the result of road recognition.

Taking in accounts these considerations and analysis, we are still working on improvements to reduce the processing time using the GPU architecture. We are also working on Self-Organizing Maps [START_REF] Kohonen | Self-organizing maps[END_REF], in order to better discriminate the road pattern and extending the recognition for different classes such as vehicles, builds, sidewalks, etc, to improve for example the classification mistakes between road and nonroad.

Fig. 1 .

 1 Fig. 1. The process overview for the first algorithm.(a) The 2D segmentation process to get the superpixel. (b) The 3D process to obtain an estimation of the drivable and non-divable areas. (c) The final result applying the ANN, which the left image represents the lines of the superpixel and the right image has the classification of non-road surface (red) and road surface (original color).

Algorithm 1

 1 Based on Texton maps, Dispton maps are the outcomes of a technique that builds two additional dictionaries over 3D information from Stereo Vision. It aims at creating meaningful clusters based on Disparity maps (I ∆ ). This technique is called by Disptonization. It encapsulates 3D information provided by the U-Disparity and V-Disparity in a dictionary of Dispton, taking then the Dispton Maps from I ∆ . The summarized Disptonization algorithm can be seen in[START_REF] Karasev | Depth invariant visual servoing[END_REF]. Disptonization algorithm:1: Process I u ∆ and I v ∆ from I∆; 2: Apply the Hmin filter on I u ∆ and I v ∆; 3: Binarize and obtain the line segments by Hough Transf.; 4: Determine the U-Dispton dictionary; 5: Determine the V-Dispton dictionary; 6: Generate the UV-Dispton maps;

Fig. 2 .

 2 Fig. 2. The Road Recognition process. For each sp i is constructed the multi-normalized histogram from the Texton maps and the Dispton maps, which are classified with Joint Boosting algorithm, explained in Section III-C

Fig. 3 .

 3 Fig. 3. The result process for Urban Unmarked (UU) Perspective image. The first row shows the original image into training set. The second row presents the ground truth, followed by the next two rows that are the HistonBoost result and Artificial Neural Network result.

Fig. 4 .

 4 Fig. 4. The result process for Urban Marked (UM) Perspective image. The first row shows the original image into training set. The second row presents the ground truth, followed by the next two rows that are the HistonBoost result and Artificial Neural Network result.

Fig. 5 .

 5 Fig. 5. The result process for Urban Marked Multi-lane (UMM) Perspective image. The first row shows the original image into training set. The second row presents the ground truth, followed by the next two rows that are the HistonBoost result and Artificial Neural Network result.

TABLE I RESULTS

 I [%] OF PIXEL-BASED FOR URBAN UNMARKED ROAD AREA EVALUATION.

		Perspective space -Training set
		Fmax	AP	Prec.	Recall	FPR	FNR
	BaseLine	80.79	86.13	79.00	82.67	3.42	17.33
	ANN	60.64	41.67	44.49	95.22	18.51	4.78
	HistonBoost	84.92	71.36	84.22	85.63	2.50	14.37
		Metric space -Testing set
		Fmax	AP	Prec.	Recall	FPR	FNR
	BaseLine	69.49	73.84	65.73	73.70	12.78	26.30
	ANN	54.17	36.86	39.50	86.19	43.92	13.81
	HistonBoost	73.51	63.07	77.36	70.03	6.82	29.97

TABLE III RESULTS

 III [%] OF PIXEL-BASED FOR URBAN MARKED MULTI-LANE

		ROAD AREA EVALUATION.
		Perspective space -Training set
		Fmax	AP	Prec.	Recall	FPR	FNR
	BaseLine	82.81	89.21	77.54	88.86	8.02	11.14
	ANN	82.04	68.79	73.30	93.14	10.58	6.86
	HistonBoost	88.72	78.45	83.92	94.11	5.62	5.89
		Metric space -Testing set
		Fmax	AP	Prec.	Recall	FPR	FNR
	BaseLine	76.17	78.42	65.02	91.95	57.89	8.05
	ANN	81.09	68.93	70.43	95.56	46.94	4.44
	HistonBoost	87.70	81.59	84.36	91.32	19.81	8.68

TABLE IV RESULTS

 IV [%] OF PIXEL-BASED FOR COMPLETE URBAN ROAD AREA EVALUATION, PERFORMED ON THE METRIC SPACE (BEV).

		Fmax	AP	Prec.	Recall	FPR	FNR
	BaseLine	75.61	79.72	68.93	83.73	21.73	16.27
	ANN	68.12	51.52	54.85	89.85	42.59	10.15
	HistonBoost	83.41	74.06	82.39	84.46	10.39	15.54

Abbreviation for red, green and blue color space.

Abbreviation for hue, saturation and value color space.

Dispton means the name of a proposed technique that encapsulates 3D information from disparity map in the called Dispton maps

Textons are the result of a technique that performs a clustering based in a given feature set, extracted from texture image.


A Histogram-Based Joint Boosting Classification for determining Urban

Road (Extended Abstract)

Giovani B. Vitor 1,2 , Alessandro C. Victorino 1 and Janito V. Ferreira 2

Abstract-Road detection for inner-city scenarios remains a difficult problem due to the high complexity in scene layout with unmarked or weakly marked roads and poor lighting conditions. This paper introduces a novel method based on multi normalized-histogram with Joint Boosting algorithm to road recognition. The approach performs three modules in parallel that are the Image Segmentation, the Texton Maps and the new one Dispton Maps. The first one applies a combination of pre-filters with Watershed Transform to make the superpixel. The last two perform a dense feature extraction based on 2D texture image and 3D disparity image to get appearance, shape and context information. At last, a discriminative model of road class is learned based on distribution of Textons and Disptons applied in Joint Boosting algorithm. The proposed work reports real experiments carried out in a challenging urban environment utilizing the modern KITTI benchmark for road areas in which meaningful evaluation can be done to illustrate the validity and application of this approach.

Index Terms-Road Detection, Computer Vision, Joint Boosting, Texton Map, Dispton Map, Watershed Transform.

I. INTRODUCTION

Road recognition is a key for autonomous driving systems, such as path planning, road following, and navigable tasks such as visual servoing and line detection. From the last years, several approaches related to vision-based road detection have been proposed, varying from mono to stereo vision sources. They aim to solve several challenging problems such as the continuously changing backgrounds in different environments, different road types, the presence of different objects and also different imaging conditions. In general, the approaches for road detection using Mono vision are based on color, texture, or the combination of both [START_REF] Alvarez | Road detection based on illuminant invariance[END_REF], [START_REF] Kong | General road detection from a single image[END_REF], [START_REF] Sturgess | Combining appearance and structure from motion features for road scene understanding[END_REF]. Despite the rich amount of information present in the color feature, which imposes less physical restrictions by performing the detection according to some similarity, it may fail due to high intra-class variability present in the dynamic nature of the scenes. The texture is scaledependent and is affected by the strong perspective in road image. The V-disparity Map [START_REF] Broggi | Obstacle detection with stereo vision for off-road vehicle navigation[END_REF] technique is an example of approaches that use 3D information as a source. However, these techniques must deal with different noise sources, such as shadow, road texture, light variations, that become hard to create the disparity map. An alternative to overcome these constraints is to combine 2D and 3D data, enhancing the detection and reducing the drawback of each approach running separately [START_REF] Vitor | A 2d/3d vision based approach applied to road detection in urban environments[END_REF].

This work proposes a novel method for road recognition in inner-city scenarios, merging 2D and 3D information, resulting in a multi normalized-histogram that model the appearance, shape and context using a Joint Boosting algorithm. Differently from others works, this approach create a novel form to represent the 3D information encapsulated on Disptons to compose jointly with the popular Textons, a different set of features, to better represent the road class given by the complex environment. A discriminative model for road class is learned based on the multi normalizedhistogram performed on super-pixel and classified using a Joint Boosting classifier.

II. MULTI HISTOGRAM-BASED JOINT BOOSTING

This section presents a brief understanding of the proposed procedure, as shown in Figure 1. The Image Segmentation block is inspired by [START_REF] Vitor | A 2d/3d vision based approach applied to road detection in urban environments[END_REF], which performs a combination of pre-filters such as the gradient of the image, the AreaClose and the Hmin, jointly with the Watershed Transform to get the super-pixel.

The Texton Map block implements the Texton method that applies the textonization process based on the extraction of various features to get the texton maps [START_REF] Shotton | Textonboost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context[END_REF]. This process of textonization construct a dictionary of Textons by using an unsupervised classifier. The unsupervised classifier is given by the K-Means algorithm where the Dictionary of Textons t i ∈ D = {t 1 , .., t K } is defined by clusters generated according to the given feature. This work uses the same set of feature's descriptor proposed by [START_REF] Ladicky | Associative hierarchical crf's for object class image segmentation[END_REF]. In the same way of the Texton maps, Dispton maps are the outcomes of a technique that builds two additional dictionaries over 3D information from Stereo Vision. It aims at creating meaningful clusters based on Disparity maps (I ∆ ). This technique is called by Disptonization. It encapsulates 3D information provided by the U-Disparity (I u∆ ) and V-Disparity (I v∆ ) in a dictionary of Dispton, taking then the Dispton Maps from I ∆ . The summarized Disptonization algorithm can be seen in Algorithm (1).

h(c i ) = aδ(d(rh, sp i ) > θ) + b , if c i = 1 κ c i , otherwise (1)
In equation ( 1

III. EXPERIMENTAL RESULTS

The proposed algorithm is experimentally validated using the URBAN KITTI-ROAD dataset. The data are categorized in three sets, called UU, UM and UMM, having each one a subset of training and test images. The experiments use the training set in the perspective space to learn the classifier and the metric space to calculate the complete evaluation of this approach, as suggested by [START_REF] Fritsch | A new performance measure and evaluation benchmark for road detection algorithms[END_REF].

The learning processes were executed separately for each category. Table I presents the final results merging all these categories. Our approach presents 83.41% of this challenging urban Kitti-Road benchmark, reaching a gain of 7.8% from BL, 15.29% from ANN and 4.66% from SP algorithm. This improvement is significative considering the complex environment where the system is exposed and seen that a small set was used for training, reaching a desired generalization. Some image samples collected from the test set can be seen in Figure 2. From the qualitative analysis, it is possible to see that the method proposed can solve the problem imposed by different obstacles such as vehicles, trees and sidewalks. Even with different conditions such as shadow areas and areas with high luminosity provided by the sun, the algorithm can acquire satisfactory approximations for the road area.

IV. CONCLUSIONS

The work proposed in this paper provides an approach for road recognition for inner-city scenarios based on the novel method that join a multi normalized-histogram with a Joint Boosting algorithm. The main advantages of the method are: (i) Its independence from prior knowledge of road assumptions; (ii) The integration of stereo vision not only to improve detection precision but also to extend the recognition capability on different urban elements; (iii) Module independency leverages the usage of parallelization and improvement on resources usage.

Fig. 1 .

 1 Fig. 1. Solution block diagram.

1 :

 1 Process I u∆ and I v∆ from I ∆ ; 2: Apply the Hmin filter on I u∆ and I v∆ ; 3: Binarize and obtain the line segments by Hough Transf.; 4: Determine the U-Dispton and the V-Dispton dictionaries; 5: Generate the UV-Dispton maps; The Road Recognition block uses a classification method derived from TextonBoost [6] to model the road class. The proposed multi normalized-histogram joint boosting algorithm builds a strong classifier as a sum of week classifiers. Based on super-pixels, the representative Textons and Disptons histograms for all segments are generated where each bin represents the cumulative number of a given cluster in their respective regions. Thereby, the Joint Boosting algorithm is an additive model of the form H(c i ) = M m=1 h m (c i ), that sum the classification confidence of M joint weak classifiers. In this case, H(c i ) represents the strong learned classifier obtained by the χ 2 metric comparisons of the weak classifiers' histogram with the response's histogram obtained from the super-pixels of the image. The output of these comparisons are modeled as a decision stump as shown in Equation (1):

  ), rh represents the random histogram comparator and sp i represents the multi normalized-histogram of the super-pixel i. The d(.) is the χ 2 function and δ(.) is a 0-1 indicator function. The weak learner gives h(c i ) ∈ {a + b, b} depending on the comparison of d(rh, sp) with a threshold θ. The constant k ci ensures asymmetrical values of positive and negative training examples.

Fig. 2 .

 2 Fig. 2. Qualitative result images for challenging scenarios. The lines represent the output result for the UU, UM and UMM categories respectively. Red color denotes false positives, blue color areas correspond to false negatives and green color represents true positives.

TABLE I RESULTS

 I [%] OF PIXEL-BASED FOR URBAN ROAD AREA.

		Fmax	AP	Prec.	Recall	FPR	FNR
	Baseline [8]	75.61	79.72	68.93	83.73	21.73	16.27
	ANN [5]	68.12	51.52	54.85	89.85	42.59	10.15
	SP [9]	78.75	77.66	72.41	86.30	18.93	13.70
	Our	83.41	74.06	82.39	84.46	10.39	15.54