Tracking multi-objects using combination feature and Mean Shift

This paper proposes a novel framework for tracking multiple targets with occlusion and illumination changing in scenes. Under our framework, the extension of Mean Shift algorithm via five global and local features: central location, width, height, area and Harris corner information, and Mean Shift (CFMS) are used to track multiple targets. To effectively track multiple objects with occluded and don´t split up the object to different part, Harris corner information as local feature is extracted by proposed double threshold Harris corner detection algorithm when there exist occluding, moreover classify the corners in the occluding region by K-NN classifier. The proposed CFMS algorithm achieved tracking of multiple targets based on feature fusion and mean shift algorithm. The experimental results have showed that the proposed method of CFMS can track multiple objects more robustly.