Object tracking using single feature often leads to a poor robustness. In this paper

a new object tracking algorithm based on multiple features fusion is presented. to alleviate the affection of object deformation and partial occlusion

it analyzes and describes the color

texture

edge and motion feature of the object using a consistent histogram model

in order to conquer the distractions in the complex background

these features are rationally fused in the framework of Auxiliary Particle filter to obtain a more satisfying approximation of the posterior distribution of the object states. A new method to estimate the fusion coefficient is also proposed to improve the fusion result. Experiment results show that our algorithm can efficiently cope with both rigid and non-rigid objects

outperforms single feature based object tracking algorithms

and has a high robustness in complex background. The comparisons with other multi-cue tracking algorithm also show the validity of the proposed algorithm.