Road traffic congestion estimation with macroscopic parameters

In this paper we propose an algorithm for road traffic density estimation, using macroscopic parameters, extracted from a video sequence. Macroscopic parameters are directly estimated by analyzing the global motion in the video scene without the need of motion detection and tracking methods. The extracted parameters are applied to the SVM classifier, to classify the road traffic in three categories: light, medium and heavy. The performance of the proposed algorithm is compared to that of the texture dynamic based traffic road classification method, using the same data base.