Study Of Multi-Target Tracking Algorithm Based On Particle Filter

Video target tracking technology is one of the hot issues in the field of computer vision and has a broad application prospect and huge economic value in military and civil such as military guidance, security surveillance and traffic monitoring. In recent years, particle filter algorithm has attracted extensive attention, which has been applied with great success to nonlinear and non-Gaussian filtering problems, such as video target tracking. Particle filters are sequential Monte Carlo executive methods based on recursive Bayesian state estimation, which can be applied to any state-space model.The multi-target tracking algorithm compared with single target tracking, is faced with more complex situation, including the changing of the number of targets, multi-target occlusion, merging and splitting, etc. According to the complex situation of video multiple targets tracking, the main objective of this paper is to solve these problems and proposes a multi-target tracking algorithm based on particle filter with high robustness and accuracy.In the paper, extensive research and simulation analysis of video multi-target tracking algorithm, including target detection algorithm and target tracking algorithm, are presented. At first, several common target detection algorithms are introduced, and Gaussian mixture modeling method is studied in detail, and a simplified shadow elimination algorithm is proposed. Secondly, particle filter theory and particle filter tracking algorithm with high reliability especially when targets are occluded are presented. Then, a fast and efficient Mean Shift tracking algorithm is introduced. Finally, a novel multi-target tracking algorithm based on Particle filter is proposed, which adopts adaptive mixed filter to track multiple targets, and can keep continuous and efficiently tracking of multiple targets when occlusion happens. Besides, the algorithms of targets initialization and termination are also improved, and enable to identify targets appearing and disappearing correctly, reduce the interference of noise and clutter, improve the accuracy and robustness of the tracking system.