Vehicle Detection And Classification Based On Video Sequences

AVC(automatic vehicle classification), the object of this paper, plays an important role in road design traffic surveillance and highway charge. Because of inadequate information obtained, difficulty of mount and lowness of dependability, traditional vehicle detection and classification with induction loop or piezoelectricity sensor can not be widely used. On the contrary, AVC based image processing and pattern recognition, settled the above-mentioned deficiency, can be widely used in auto charge, park lot management and highway surveillance and become the direction of AVC development.General speaking, AVC based image processing and pattern recognition can be decomposed into three steps such as moving segmentation, object tracking and vehicle classification. In this paper, background without any vehicle can be reconstructed from video sequences and object can be detected only that we subtract background from current image. Followed segmentation, multi-object tracking is necessary in order to count vehicles correctly and acquire vehicle speed. We performed object region tracking based on its characters such as area, location and velocity. Using this method, region merger and split can be resolved in some degree. In vehicle classification period, because deep information is absent, it's a very difficult task to obtain vehicle characters from images. Vehicle recognition based model match method is adopted and we can match segmentation edges and model edges with Hausdorff distance. This method is not sensitive to deformation and noise. In order to get vehicle speed and perform model matching, perspective matrix is calculated based on pin-hole model. All above algorithm is realized and optimized on TrimediaDSP l300.