Improved Vehicle Detection Algorithm in Heavy Traffic for Intelligent Vehicle

1 Abstract —Despite significant progress in vehicle detection over the last few decades, vehicle detection performance in heavy traffic is still inadequate. In this paper, we propose a new algorithm for vehicle detection in heavy traffic to improve detection performance. It uses two proposed segmentation methods, namely, the disparity map-based bird's-eye-view mapping segmentation method and the edge distance weighted conditional random field (CRF)-based segmentation method. Our experimental results show that the proposed algorithm outperforms conventional algorithms. The improvements in performance range from 10.8 % to 20.5 % increase in F-measure.


I. INTRODUCTION
Forward vehicle detection is one of the most important technologies in an intelligent vehicle, which is closely related to autonomous driving, collision prevention, etc [1].Many researchers have proposed vehicle detection methods using various sensors [2], [3].Camera sensors in particular have many advantages, such as possession of a large amount of information and low cost compared to other sensors.Thus, the vehicle detection algorithms based on images have been studied for a long time [4].Widely studied methods include the AdaBoost classifier [5] using intensity information and u/v-disparity [6] or column detection [7], [8] using a disparity map.The Adaboost classifier can cause many false alarms while detecting vehicles in occluded situations, such as heavy urban traffic, since the classifier solely uses intensity-based features.A disparity map-based vehicle detection method can minimize the problems by using three-dimensional information.However, it is also difficult to detect vehicles accurately in heavy traffic because of a stereo matching error and the precision of the disparity map.
In this paper, we focus on the improvement of vehicle detection performance in heavy traffic based on the proposed segmentation methods.In most cases, extracting exact obstacle areas from images is a very important step for improving vehicle detection performance.Although a disparity map can provide approximate boundaries of obstacles and backgrounds, there are still many limitations in relying on only the disparity information.Intensity and edges are other important cues for segmentation in small obstacle areas, namely results of disparity map-based obstacle detection and segmentation.Thus, two proposed segmentation methods, disparity map-based bird's-eye-view mapping segmentation and edge distance weighted conditional random field (CRF)-based segmentation are proposed here for accurate vehicle detection.The segmentation method which is suitable for on-road vehicle detection, has not been proposed yet.
The remainder of this paper is organized as follows.Section II presents the proposed disparity map-based obstacle detection and segmentation algorithm.Section III describes the proposed intensity-based obstacle segmentation algorithm.In Section VI, we describe the area selection and verification method.The experimental results are presented in Section V. Finally, conclusions of this paper are given in Section VI.

II. DISPARITY MAP-BASED OBSTACLE DETECTION AND BIRD'S-EYE-VIEW MAPPING SEGMENTATION
Our proposed algorithm is described in detail according to steps and a block diagram is shown in Fig. 1.First, all road obstacles are detected by using road feature information extracted by a previously proposed method [7].In our previous work [7], we found that it was very important to extract road feature information robustly to improve detection performance in various traffic situations.However, there are still many obstacles and backgrounds in the detected areas.Thus, each area needs to be segmented more accurately.imply the baseline and pitch, respectively.Each camera coordinate system is defined as Rcl (the left camera) and Rcr (the right camera), and the image plane is also defined as I(u,v).The detected areas in the disparity map are mapped into a bird's-eye-view using stereo vision modelling equations and projection of the X-Z plane, which can be calculated by: where P(X, Y, Z, 1) T is a point in the world coordinates, namely, the lateral, vertical, and longitudinal positions,  The positions of obstacles are very well, because the results of ( 1)-( 3) can be represented on a flat plane by projecting them on an X-Z plane.Thus, the obstacles can be segmented more accurately and easily with disparity map-based bird's-eye-view mapping.Segmentation in the bird's-eye view consists of two steps, row segmentation and post-segmentation.Row segmentation is performed by detecting peaks and valleys in each row of the bird's-eye view.Post-segmentation is then performed additionally, because the obstacles are divided excessively in the case of long obstacles such as guide rails, median barriers, etc.When the centre position and disparity value of the segmented obstacles are changed regularly, the obstacles are regarded as long obstacles and remerged in the post-segmentation.Thus, very long obstacles can be identified and removed.We can also remove an obstacle whose height is too tall or too short using the disparity information.From (2), the obstacle height where Y1-Y2 is the obstacle height in the world coordinate.Thus, if we predefine a vehicle's height (Y1-Y2), an obstacle whose height is longer or shorter than the vehicle's height can be removed in the disparity map-based segmentation.The results segmented in the bird's-eye-view are reconverted into the disparity map.These disparity map-based segmentations improve detection performance in heavy traffic thanks to accurate identification of obstacle position and removal of unnecessary obstacles.However, additional segmentation is needed due to the limitation of the disparity map.The entire procedure of disparity map-based obstacle detection and segmentation is presented in Fig. 3.

III. EDGE DISTANCE WEIGHTED CRF-BASED SEGMENTATION
In this paper, edge distance weighted CRF-based segmentation using a new pairwise potential function is proposed to obtain more accurate results from the above results.Let S = {1, ..., n} be an index in the image lattice, where n is the number of pixels in the observed image.Let ) exp{ ( , ) ( , , )}, where z is the partition function, The unary potential is defined as the Gaussian likelihood  are the mean and standard deviation of intensity associated with the class indicated by label x at location i, respectively.In the case of pairwise potential, a novel potential for accurate vehicle detection is proposed, which is one of the major contributions of this paper.The edge and intensity are important cues for segmentation in small obstacle areas, and the vertical edge in particular is a good standard for separating the vehicle from the background or other vehicles in close proximity.The proposed pairwise potential is defined as: , 2 Generally, the that the current pixel has the same label as its neighbours is high in regions that do not have an edge.On the contrary, the probability is low in regions that have an edge.In other words, the farther away from the edge the current pixel is, the more important the label information of the neighbours, such as pairwise potential, is.On the other hand, the closer to the edge the current pixel is, the more important the feature data (intensity), such as unary potential, is.Thus, the distance information between the current pixel and the edge is utilized in the proposed CRF.The relative importance of pairwise and unary potential is determined by the edge distance.The distance information is obtained from the distance transformation using a vertical edge image extracted from each detected area.Each edge distance is normalized, and the mean is utilized as the final edge distance.
Note that ) , , ( Y x x j i ij  also uses other information, such as the spatial distance and the difference of intensity between two pixels.The shorter the pixel distance and the smaller the difference of intensity are, the higher the probability is that the current pixel has the same label as its neighbours.The Euclidean distance and the Gaussian model are utilized to measure the spatial distance and the difference of intensity between two pixels, respectively.
The model parameter  is estimated by using the pseudo-likelihood method and is defined as , ) , , ( max arg where ˆis the estimated parameter, M is the number of training images, and i N x is the label of the neighbour of pixel i.Other parameters, i x  and i x  are first calculated directly from the same labelled pixels from the initial label status.However, these parameters are updated each time during the inference.The iterated conditional modes (ICM) algorithm [11] Thus, the label of each pixel i x ˆis estimated using ˆarg max ( , ).  are updated every time, and the neighbour pixels' labels obtained in the current iteration are utilized immediately when the pairwise potential of the current pixel is calculated.In other words, the current estimated data are updated instantly to be utilized when the label of the next pixel is determined.

IV. AREA SELECTION AND VERIFICATION
After segmentation processing, the isolated small areas are removed by image processing, and size-based area selection is performed.The largest area is selected because a vehicle has almost unvarying intensity and has the largest size in the segmented results.However, if an area whose size is greater than half size of the largest area exists, the area is also selected.Finally, the selected area is verified with respect to whether it is a vehicle or not [7].The entire process of intensity-based obstacle segmentation and verification is presented in Fig. 4.

V. EXPERIMENTAL RESULTS
The proposed vehicle detection algorithm was evaluated by application to heavy traffic images captured by our stereo vision system.Our system architecture and a photo of the system installed in the test vehicle are presented in Fig. 5.The belief propagation algorithm is utilized to make the disparity map, which is implemented on FPGA hardware for real-time processing [12].Our algorithms are implemented using vehicle recognition software with C++.(a) Adaboost classifier [5] (b) Column detection [7] (c) Our method Fig. 6.Vehicle detection results in heavy traffic: (a) adaboost classifier [5]; (b) column detection [7]; (c) our method.
Manuscript received March 1, 2014; accepted September 14, 2014.This work was supported by DGIST R&D Program of the Ministry of Education, Science and Technology of Korea (13-IT-02).

Fig. 1 . 1 DaeguFig. 2 .
Fig. 1.Block diagram of vehicle detection algorithm.An automotive stereo vision system model is shown in respectively; l u and r u are the horizontal positions of the point in the left and right images, respectively; and v is the vertical position of the point.Here, ( 0 u , 0 v ) is the center of the image, and d is disparity, which can be expressed by r l u u  ; f is the focal length of the camera; and m is the number of pixels per unit distance the image plane.
, where i x is one of the labels in L = {1, ..., c} at location i, and c is the number of labelsimage, where i y is the known pixel intensity at location i. CRF-based calculation of the label distribution for segmentation[9] can be performed by 1 (


are unary and pairwise potentials, respectively, and Ni denotes the neighbours of pixel i.


is the model parameter, wij is the weighting parameter, i N  is the standard deviation of intensity in the neighbours including the current pixel, delta.Here, Di (or Dj) is the normalized distance transform value, di (or dj) is the distance transform value [10] of the pixel, and relationship between the current pixel and the neighbours, and it uses the distance transform as the weighting ( ij w ).

Fig. 5 .
Vehicle detection system for intelligent vehicle: (a) system architecture; (b) system installed in test vehicle.
is utilized to optimize the proposed model.