A novel method to detect vehicles is presented in the paper. Assumption of the vehicle is made using the geometrical features of the vehicle rear by the statistical histogram. Then hypothesis is verified using the property of the shadow cast by the car according to a prior acknowledgement of traffic scene. Finally, the vehicle detection is realized by hypothesis and verification of objects. The experimental results show the efficiency and feasibility of the method

Li, Qing

Sun, F.C.

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International Journal of Computers, Communications & ControlVol. I (2006), No. 4, pp. 93-99Obstacle Detection in Cluttered Traffic Environment Based onCandidate Generation and ClassificationQing Li, F.C. SunAbstract: A novel method to detect vehicles is presented in the paper. Assumptionof the vehicle is made using the geometrical features of the vehicle rear by thestatistical histogram. Then hypothesis is verified using the property of the shadowcast by the car according to a prior acknowledgement of traffic scene. Finally,the vehicle detection is realized by hypothesis and verification of objects. Theexperimental results show the efficiency and feasibility of the method.Keywords: objects hypothesis, objects verification, statistical histogram, vehiclesdetection1 IntroductionThe statistics of traffic accidents show that drivers have to face threats mainly from other vehicles. Itis very significant to detect preceding vehicles in real time for keeping safe distance and avoiding colli-sion. At the same time, vehicles detection is the precondition of safe driving and primary requirement ofdriver assist systems, active safety systems and automatic vehicles[1]. Many methods based on vision areused to detect vehicles, such as according to symmetry, shadow, texture, horizontal/vertical edge, color,stereo vision, depth, movement, template, view point, information fusion and parallax. We mainly detectpreceding vehicles, including all vehicles running along same direction, even in other lanes, by visionsensors. Furthermore, head-on vehicles shall not be considered because we emphasize on systems forstructural road where contrary lanes are separated by rail fence. A method based on objects assumptionand verification for vehicle detection is suggested in the paper. Firstly, vehicles assumption is made bystatistical histogram according to geometrical features. Secondly, the assumption is verified according toprior acknowledge. Lastly, the vehicles detection is realized. The experimental results show the methodis valid and feasible.2 Object assumptionThe colorful video is gathered and conversed into frames of images. The images are preprocessedin the procedure of objects assumption. Then the potential vehicles are located in the whole image byseveral standards according to features of vehicles.A.Finding Edge Features in the ImageFirstly, the object area is found approximately by camera calibration. Secondly, the color space istransformed in order to utilize color information effectively. Firstly, the horizontal gradient, the verti-cal gradient and the characteristic gradient are calculated in different color channel. In order to detectvehicles on the road, some features of vehicles are analyzed, including symmetry and edges. Vehiclesmainly consist of horizontal structure, especially when they are observed from the rear. Though otherhorizontal structures exist in the road scene, the areas can usually be marked and their physical feasibilitycan be judged. Some experiments show horizontal edges are useful cue to confirm area of interest wherevehicles appear possibly. In order to obtain horizontal edge response Ih and vertical edge response Iv,a direct edge filter is used. The input image is masked by two n× n template. The values of Ih and Ivis compared to obtain an edge response map Ir [2].In fact, man-made objects have more obvious edgeresponse than natural objects. The value Ir is relatively distinct for vehicles, so it is regarded as a basicCopyright c© 2006 by CCC Publications94 Qing Li, F.C. Sunfeature to detect vehicles(Fig.1)(Top: horizontal addition of the vertical histogram; bottom: horizontallines representing vehicle (overlapped on the original image)).Figure 1: Object location in the vertical directionB. Statistical Histogram ProcessFirstly, we shall determine the possible location of vehicles in the vertical direction. The statisticalhistogram is formed by adding values of the edge responding map in a line and smoothed. The shiftmean is used to smooth the curve in the paper. The potential vehicle is located by the local maximumof the smoothed curve. It is the most important to find and detect peak for segmenting the maximum ofthe histogram. Some methods are suggested to choose obvious mode. By examining the steepness orarea of the peak value[3], the peak value will be detected. If it is not enough steep or big, the peak willbe deleted[4]. The road surface is supposed as flat. The vehicles on road show symmetrical shape andcertain ratio of high to width.(0.8-1.2).The ratio is set to 1.0 in the paper. The other feature is the sizeof vehicles in the image. The object will be far from the driver if the size is very little and will not beconsidered. The width of object responds the distance between the object and the driver. The smallestcar far from 100 meter responds the width of 30 pixels. If the imaging width of the object is over 30pixels, the distance will exceed 100 meters. Usually, the distance of 100 meters is enough to respond fordrivers of the speech of vehicles is below 120 km/h.Though the approximate position of vehicles can be determined by the above work, the imaging sizeof vehicles can’t be solved and more work will be finished. The possible position locates the middlebottom of vehicles in Fig.2(top: vertical addition of the horizontal histogram; bottom: vertical lineslocated(overlapped on the original image) and possible position of vehicles). The area of interest ofposition (u0,v0) is as A(u,v),u ∈ (u0− h1,u0 + h2),v ∈ (v0− v1,v0 + v2) (Fig. 3). The vertical edgesof vehicles is searched in the area of interest A(u,v). The statistical histogram of the area of interest isformed by the edge gradient in the vertical direction. The peak value of the left edge and the right edgeof the vehicle can be found by the method. The peak value just near the candidate point is chosen if manypeak values exist. No vehicles exist in the area of interest if only one peak value exist. The symmetrydistance is introduced and obtained by calculating the mean square error of gray value gle f t and gright[5].Obstacle Detection in Cluttered Traffic Environment Based onCandidate Generation and Classification 95SD = E[(gle f t −gright)2] (1)Figure 2: Object location in the horizontal directionFigure 3: The area of interest determined.If the SD is below a certain value, the obstacle more possibly exist(Fig.4). The area with maximumis defined as candidate area of vehicles(Fig.5).3 Assumption verificationEven a potential object meets three standards, the assumption would be further verified because ofweak grouping condition to seek potential vehicles. The object is verified based on shadow. Manymethods are used to detect vehicles by utilizing the shadow below vehicles[6][7][8].4 ExperimentDifferent types of vehicles are detected in the different road and weather condition. The variationalPr/PR, Pg/PG and Pb/PG are illustrated in Fig.6 when the bar box moves. The different areas detected96 Qing Li, F.C. SunFigure 4: The vertical edges of the vehicle being located in the area of interestFigure 5: The candidate objectObstacle Detection in Cluttered Traffic Environment Based onCandidate Generation and Classification 97by object assumption and verification are showed in Fig.7. The position of preceding vehicles can bedetermined by combining with lane detection in Fig.8.Figure 6: Blue ratio, green ratio and red ratio in the vertical candidate areaFigure 7: The different areas detected by object assumption and assumption verification5 Summary and ConclusionsBecause size, shape and color of vehicles are different, it is very challenging to detect vehicles byoptical sensors. Additionally, surface of vehicles relies on their pose and is affected by ambient objects.It is difficult to control complicated outdoor environment. The method is composed of object assumptionand assumption verification. Horizontal edge and symmetry of vehicle rear is used to seek candidatevehicles in assumption. Shadow is used to verify vehicles in verification. Experimental results showfeasibility of the method.98 Qing Li, F.C. SunFigure 8: Some results of vehicle detection6 AcknowledgeThis work was jointly supported by the National Natural Science Foundation of China (Grant No:60334020, 60321002, 60474025, 60504003 and 90405017), the National Excellent Doctoral Disser-tation Foundation (Grant No: 200041), the National Key Project for Basic Research of China (GrantNo: G2002cb312205) and the Specialized Research Fund for the Doctoral Program of Higher Educa-tion(Grant No: 20050003049).References[1] M. Bertozzi, A. Broggi,A. Broggi,A. Fascioli,Vision-based intelligent vehicles: state of the art andperspectives, Robotics and Autonomous Systems, Vol. 32, pp. 1-16, 2000.[2] N. Matthews, P. D. Charnley, C. Harris, Vehicle detection and recognition in greyscale imagery,Control Engineering Practice, Vol. 4, pp. 473-479, 1996.[3] D. C. Tseng, C. H. Chang, Color segmentation using perceptual attributes, Proceedings of IEEEInternational Conference on Image Processing, pp. 228-231, 1992.[4] H. D. Cheng, Y. Sun, A hierarchical approach to color image segmentation using homogeneity, IEEETransactions on Image Processing, Vol. 9, No. 12, pp. 2071-2082, 2000.[5] H. Mori, N. M. CharkariY, T. Matsushita, On-line vehicle and pedestrian detections based on signpattern, IEEE Transactions on industrial electronics, Vol. 41, No. 4, pp. 384-391, 1994.[6] M. Y. Chern, B. Y. Shyr, Locating nearby vehicles on highway at daytime based on the front visionof a moving car, Proceedings of Robotics and Automation, Vol. 2, pp. 2085-2090, 2003.[7] S. Kyo, T. Koga, K. Sakurai, S.OkazakiM, A robust vehicle detecting and tracking system for wetweather conditions using the LMAP-VISION Image Processing Board, Proceedings of IEEE Inter-national Conference on Intelligent Transportation Systems, pp. 423-428, 1999.Obstacle Detection in Cluttered Traffic Environment Based onCandidate Generation and Classification 99[8] S. Nadimi, B. Bhanu, Physical models for moving shadow and object detection in video, IEEETransactions on Pattern Analysis and Machine Intelligence, Vol. 26, No. 8, pp. 1079-1087, 2004.Q. Li, F.C. SunTsinghua UniversityDepartment of Computer Science and TechnologyBeijing, ChinaE-mail: qli@mail.tsinghua.edu.cnReceived: November 10, 2006

Obstacle Detection in Cluttered Traffic Environment Based on Candidate Generation and Classification

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Obstacle Detection in Cluttered Traffic Environment Based on Candidate Generation and Classification

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