Multi-Vehicle Tracking Based Traffic Illegal Parking Detection

Abstract

Vehicle detection and tracking are the foundations of traffic violation punishment. The current methods have more parameters to be adjusted and can not adapt to the complex traffic scenes. For the purpose of practice application, we propose an illegal parking detection algorithm based on vehicle tracking theory. In order to filter the dynamic interferences such as vehicles parking for a long time, moving vehicles overlapping, and the vehicle losing momentarily from the video, a Gaussian mixture model is combined with the correlations among adjacent pixels to update background and eliminate noises. In allusion to complex traffic scenes, a fast multi-object tracking algorithm is presented to track occluded vehicles effectively. This method predicts vehicle trajectory by Kalman filter and achieves real-time object tracking state judgment by using the feature matching matrix. The experimental results show that this method can track multiple moving vehicles effectively and has high detection precision for illegal parking incidents.