High-efficient detection of traffic parameters by using two foreground temporal-spatial images

Abstract

Real-time detection of vehicular volume, mean speed and vehicle type has important significance, but the existing video-based detection methods are not satisfactory at processing speed and accuracy. This paper proposes a high-efficient method to detect all the three parameters from two foreground temporal-spatial images (TSIs) directly, which are obtained from two virtual detection lines (VDLs) in video frames. Such usage of the TSIs provides a feasible approach to solve the problems of vehicle occlusion, mean-speed estimation, and vehicle classification without using original frame images. Firstly, for improving the accuracy of detection, during generation of the foreground TSIs, we set a small-wide region of interest for each VDL and propose a local background subtraction method and an improved moving shadows elimination method to eliminate unwanted interferences. Then, in order to reduce the calculation complexity, during extraction of the parameters, we analyze the feasibility of vehicle classification direct from the foreground TSIs, and propose a method to extract shape-feature vector from the TSIs directly. The dependence on original frame images is minimized, so the pressing speed is improved obviously. Experimental results prove the feasibility and efficiency of the proposed method.