A New Robust Object Tracking Algorithm by Fusing Multi-features
一种鲁棒的多特征融合目标跟踪新算法

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.