一种鲁棒的多特征融合目标跟踪新算法

仅利用单一的目标特征进行跟踪是大多数跟踪算法鲁棒性不高的重要原因。提出了一种新的多特征融合目标跟踪算法，该算法将目标的颜色、纹理、边缘、运动特征统一使用直方图模型进行描述，以降低算法受目标形变和部分遮挡的影响，在Auxiliary粒子滤波框架内将所有特征观测进行概率融合，以突出状态后验分布中目标真实状态对应的峰值，从而有效避免了复杂背景的干扰，并给出了一种有效的融合系数计算方法，使融合结果更加准确可靠。实验结果表明，该算法能同时处理刚性与非刚性目标的跟踪，较单一特征的跟踪算法具有明显的优势，对复杂背景下的跟踪具有较高的鲁棒性。与现有多特征融合算法的比较也证明了本文算法的有效性。

A New Robust Object Tracking Algorithm by Fusing Multi-features

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.