Abstract
An increase in demand for traffic monitoring of densely populated areas has been the subject of many discussions concerning the security on the road. Cameras and other sensors are used to monitor, identify and manage potential incidents that can occur. Therefore, there is a need for computer vision system and algorithms to detect and track vehicles, which will facilitate the management of traffic and driving assistance. In this paper, a vehicle detection and tracking system is proposed using an efficient background cancelation technique and Haar-like features with a modified Adaboost algorithm in a cascade configuration for maximum accuracy and robustness. The proposed system is implemented on a passenger car with a camera mounted at the rear to detect vehicles behind it. Video data are collected and processed in real-time and the performance of the system is verified experimentally.
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