CF-SOLT: Real-time and accurate traffic accident detection using correlation filter-based tracking

Abstract

Traffic accident detection using video surveillance is valuable research work in intelligent transportation systems. It is useful for responding to traffic accidents promptly that can avoid traffic jam or prevent secondary accident. In traffic accident detection, tracking occluded vehicles in real-time and accurately is one of the major sticking points for practical applications. In order to improve the tracking of occluded vehicles for traffic accident detection, this paper proposes a simple online tracking scheme with correlation filters (CF-SOLT). The CF-SOLT method utilizes a correlation filter-based auxiliary tracker to assist the main tracker. This auxiliary tracker helps prevent target ID switching caused by occlusion, enabling accurate vehicle tracking in occluded scenes. Based on the tracking results, a precise traffic accident detection algorithm is developed by integrating behavior analysis of both vehicles and pedestrians. The improved accident detection algorithm with the correlation filter-based auxiliary tracker can provide shorter response time, enabling quick identification and detection of traffic accidents. The experiments are conducted on the VisDrone2019, MOT-Traffic and Dataset of accident to evaluate the performances metrics of MOTA, IDF1, FPS, precision, response time and others. The results show that CF-SOLT improves MOTA and IDF1 by 5.3% and 6.7%, accident detection precision by 25%, and reduces response time by 56 s.