为更好地应对跟踪过程中复杂的场景变化问题，提出一种采用多种特征融合进行跟踪的方法。算法在粒子滤波的框架下，通过在跟踪过程中对每一个特征进行不确定性度量，计算动态的特征权值，从而完成了自适应的特征融合。利用颜色、空间和纹理特征的互补特性，提升了算法的跟踪性能。实验结果表明，算法能够很好地适应目标尺度、旋转、运动模糊等复杂场景的变化。与近年来流行的算法相比，所提出的算法具有明显优势，能够很好地完成跟踪任务。

In order to deal with complex scene change problem in the tracking process, we propose a tracking algorithm via multiple feature fusion. Under the framework of particle filter, dynamic feature weights are calculated by making an uncertain measure of each feature in the tracking process, which results in adaptive feature fusion. The algorithm uses the complementarity of color, space and texture features to improve the tracking performance. Experimental results show that the algorithm can adapt to complex scene changes such as scale, rotation and motion blur. Compared with traditional algorithms, the proposed algorithm has obvious advantages to complete the tracking task.