Design and Implementation of Multiple-Vehicle Detection and Tracking Systems with Machine Learning

A vehicle detection system is realized in two stages: hypothesis generation (HG) and hypothesis verification (HV). HG adopts frame division and shadow detection to find possible candidates of vehicles within a plausible region of the image frame. Then, during HV, object ratio constraint is first used to eliminate unreasonable hypotheses. Afterward, based on the training results of the support vector machine (SVM) with the proposed vehicle feature extraction, the kernel function is found and employed in the classifier to find the real vehicles. Both pure software and combined software/hardware (SW/HW) implementations of the proposed vehicle detection system are presented where the combined SW/HW implementation can achieve correct detection rate of more than 90% in most test conditions.

[1]  Zehang Sun,et al.  On-road vehicle detection using evolutionary Gabor filter optimization , 2005, IEEE Transactions on Intelligent Transportation Systems.

[2]  Umesh N. Hivarkar,et al.  Vision based preceding vehicle detection using self shadows and structural edge features , 2011, 2011 International Conference on Image Information Processing.

[3]  Jianwei Zhang,et al.  Real-time vehicle detection based on Haar features and Pairwise Geometrical Histograms , 2011, 2011 IEEE International Conference on Information and Automation.

[4]  Erdinç Altug,et al.  Increasing driving safety with a multiple vehicle detection and tracking system using ongoing vehicle shadow information , 2010, 2010 IEEE International Conference on Systems, Man and Cybernetics.

[5]  D. Scharstein,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001).

[6]  Zehang Sun,et al.  A real-time precrash vehicle detection system , 2002, Sixth IEEE Workshop on Applications of Computer Vision, 2002. (WACV 2002). Proceedings..

[7]  Wen-Hsiang Tsai,et al.  Real-Time Security Monitoring Around a Video Surveillance Vehicle With a Pair of Two-Camera Omni-Imaging Devices , 2011, IEEE Transactions on Vehicular Technology.

[8]  Jae Wook Jeon,et al.  FPGA Design and Implementation of a Real-Time Stereo Vision System , 2010, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  Zehang Sun,et al.  Monocular precrash vehicle detection: features and classifiers , 2006, IEEE Transactions on Image Processing.

[10]  Zehang Sun,et al.  On-road vehicle detection: a review , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  A. Broggi,et al.  A cooperative approach to vision-based vehicle detection , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[12]  Thomas Kalinke,et al.  An image processing system for driver assistance , 2000, Image Vis. Comput..

[13]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.