A novel video based system for detecting and counting vehicles at user-defined virtual loops

This paper presents a new system for detecting and counting vehicles in urban traffic videos at user-defined virtual loops. The proposed method uses motion coherence and spatial adjacency to group sampling particles in urban video sequences. A foreground mask is created using Gaussian Mixture Models and Motion Energy Images to determine the preferable locations that the particles must sample, and the convex particle groups are then analyzed to detect the vehicles. After a vehicle is detected, it is tracked using the similarity of its colors in adjacent frames. The vehicles are counted in user-defined virtual loops, by detecting the intersections of the tracked vehicles with these virtual loops. The experimental results based on different traffic videos, with a total of 80,000 video frames, suggest that our approach potentially can be more reliable than comparable methods available in the literature.