The Research And Implementation Of ITS Information Collection Subsystem

ITS information collection subsystem, the object of this thesis, plays an important role in road design, traffic surveillance and highway charge. Because of inadequate information obtained, difficulty of mount and lowness of reliability, traditional vehicle detection and classification with induction loop or piezoelectricity sensor can not be widely used. On the contrary, ITS information collection subsystem based on image processing and pattern recognition, settled the above-mentioned deficiency, can be widely used in auto charge, parking lot management and traffic surveillance and become one of the directions of ITS development. Generally speaking, the method of ITS information collection subsystem based on image processing and pattern recognition can be decomposed into three steps such as movement segmenting, object tracking and vehicle parameters getting. In this thesis, 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 feature from images. Vehicle recognition based on the ratio of length to width method is adopted and we can match segmentation edges and model edges with classification table. 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. At last, some necessary steps, such as the recognition of road, ratio of occupied road are realized.