A Robust Traffic Parameter Extraction Approach for Surveillance System at Urban Intersection

For a typical urban intersection, moving vehicle shadow and vehicle-pedestrian mixed conditions exist in traffic scene commonly. These interfering factors lead to a very low correct rate of the traffic parameters extraction. This paper presents robust traffic parameters extraction (RTPE) approach for traffic surveillance system at an urban intersection, which contains three key algorithms. First, a texture-based vehicle segmentation (TVS) algorithm is introduced to solve the moving shadow problem. Second, we propose an image exponential entropy-based vehicle exist detection (IEE-VED) algorithm to reduce the noise interference by pedestrians at the intersection, and we extract vehicle features from raw visual information to determine whether there is a vehicle in the detection zone. On this basis, the traffic parameters measurement (TPM) algorithm is introduced to calculate some important traffic parameters of the intersection for traffic management and traffic jam detection, such as traffic flow, time occupancy ratio, space mean speed and the difference of IN/OUT traffic flow. Experimental results indicate that the proposed RTPE approach is effective for traffic parameters extraction, and these parameters can truly reflect the prevailing traffic condition.