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We propose a novel multiple targets tracking algorithm combining Mean Shift and Particle Filter, and enhance the performance with target model update process. Mean Shift has a low complexity, but is weak in dealing with multi-modal probability density functions (pdfs). Particle Filter is robust to the partial occlusion and can deal with multi-modal pdfs. In real application, illumination conditions, the visual angle as well as object occlusion can change target appearance, thus influence the quality of Particle Filter. For multi-target tracking task, the mutual occlusion of targets and computational complexity are important problems for tracking system. In this paper, Mean Shift algorithm is embedded into Particle Filter framework to get stable tracking and reduce computational load. To overcome the target appearance changes caused by illumination changes and object occlusion, targets model are updated adaptively during tracking. Experimental results show that our tracking system can robustly track multiple targets with mutual occlusion and correctly maintain their identities with smaller number of particles than Particle Filter.

ROBUST MULTI-TARGET TRACKING USING MEAN SHIFT AND PARTICLE FILTER WITH TARGET MODEL UPDATE