Cars Tracking and Counting at Night
Yuan Been Chen · Applied Mechanics and Materials · 2012
This work proposes a robust scheme to automatically tracking and counting cars in the traffic surveillance. In the proposed method, pixels at a specific position of successive image frames are first processed by the modified iterative threshold selection technique to establish the background model. Second, an original image is subtracted by this background to obtain a difference image that is performed with the differential image between an original image and its precedent neighboring image to yield an image with initial contour points of moving objects. Third, the robust edge-following scheme manipulates these contour points to produce closed-form objects. Particularly, two headlights of a car are merged with their corresponding reflective lights on the ground to yield two light objects for a car extraction at night. As compared to the conventional methods, the proposed method is demonstrated to have the best accuracy of moving object extraction. Finally, object motion connection is effectively employed to track object paths and compute the number of moving cars. The practical implementation reveals that the proposed method can precisely and reliably estimate a traffic amount.