Noise reduction algorithm of vehicle detection in intelligent transportation system

Abstract

Vehicle detection is one of the key technologies in intelligent transportation system (ITS), and it is an important stage of vehicle tracking in visual surveillance. Due to the clutter of traffic scenes, the captured video sequence involves many large noises. In analyzing human vision, the non-object area and shape suppression are defined, and a new noise-removing algorithm is proposed. To reduce the computational complexity, the sequential algorithm is improved. Experimental results show the effectiveness and efficiency of the algorithm in extracting the moving vehicles in a cluttered scene. The output of the vehicle's contour is very complete and accurate, and can be used in vehicle tracking system to improve the performance.