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.