Study On Vision Tracking Method Of Moving Object Under Urban Traffic Scenes

Automobile as an important vehicle in the modern world, it brings so much convenient to people. Just as every corn has two sides, the traffic accidents also threaten the people's livies and property safety. Decreasing the traffic accident and raising the safety level of road traffic become the crying request of the society. So Studying on vision tracking method of moving object under urban traffic scenes and core technology to decrease the traffic accident has an important academic significance and social values. Urban vehicle target tracking is an important part of the vehicle active safety earlywarning system. Meanwhile, the moving target tracking is an important issue of computer vision research. This dissertation whose research objects are vehicle targets under the urban traffic scenarios is based on the intelligent vehicle active safety forewarning system. In this dissertation, we researched the application of Mean shift Particle filter in the field of object tracking. First, a method, which combines Mean shift and Kalman filter, is proposed. At the same time, an occlusion coefficient is designed as the evidence of target occlusion. Second, histogram-based nuclear particle filter algorithm, put forward an adaptive template updating method, effectively raising the real-time algorithm, while avoiding excessive update of the target model as well as frequent update. Main study content has been summarized as follows:①Describe our current traffic conditions, and outline the main reasons of road traffic accidents. Highlight the significance to achieve the human visual system function of computer vision technology which integrates the computer science, machine vision, image engineering, pattern recognition, artificial intelligence and other advanced technologies. Analysis the main target tracking methods and technical challenges currently facing.②After analyzing the theoretic limitation of the Mean shift to track fast moving targets in complex background, a method, which combines Mean shift and Kalman filter, is proposed. Firstly, the initial position of Mean shift is predicted by Kalman filter at present, and then the Mean shift is utilized to track the target position around the initial position. Meanwhile, the Bhattacharyya coefficient is adopted to measure the comparability between the target model and the candidate model. An occlusion coefficient is proposed as the evidence of occlusion. Experiments based on the vehicle objects in the city are carried out, and the simulation results show that the tracking stability for the fast moving targets, even in occlusion, is improved significantly with the proposed method.③Discuss the histogram-based nuclear particle filter, and analysis the advantages of particle filter applied to object tracking relative to Mean shift and Kalman filter algorithm. After highlighting Particle filter algorithm modeling issues, including the dynamic model and observation model, a goal of adaptive template updating method is proposed. Describe the choice of kernel function, bandwidth determination and the determination of target location, then, show the structure and flow chart of the algorithm in this chapter. Finally, test the algorithm of this chapter by experiments and get a very good effort.Finally, the conclusion of whole research work in the paper is given. Furthermore, the further work and research prospects are introduced.