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The conventional kernel color histogram based particle filter (CHGPF) object tracking algorithm suffers from cluttered backgrounds and illumination changes and results in tracking lost easily. In this paper, a particle filter based object tracking algorithm fusing color and haar-like feature (CHAPF) is proposed. As haar-like features descript the object structure in multi-scale model, the haar- like feature based Semi-Supervised On-line Boosting (HSEMOB) tracking algorithm can well distinguish between the object and background, and be adaptive to changes of the object and environment by updating haar-like features. In our proposed algorithm, the results of HSEMOB and CHGPF are weighted adaptively to obtain a new particle filter weights. So, the color-based and haar-like based observations are fused into the framework of particle filter to make it adapt to the change of scene. Algorithm performance is tested with the environment complex videos. Experimental results show that the algorithm has better accuracy compared to CHGPF and HSEMOBPF for the scene of an object has similar appearance to the background or changes of illumination.

Adaptive Fusion Color and Haar-like Feature Object Tracking Based on Particle Filter