在复杂场景下，传统的粒子滤波跟踪算法较难定位目标。针对此问题，提出了一种基于在线特征选择的粒子滤波跟踪算法。该算法首先在线、自适应地通过Fisher 判别准则，从16 个不同的颜色特征空间中选择最能区分目标及其邻近背景的1 个最佳特征空间，然后在这个最佳特征空间中用基于统计直方图的粒子滤波算法跟踪目标。试验结果表明，该算法鲁棒性和准确性较好，在光照变化、目标自身发生形变和遮挡情况下能够准确地对目标进行跟踪。

Traditional particle filter tracking algorithm is difficult to get accurate target location in the complicated circumstance. In order to solve these problems, a novel particle filter tracking algorithm based on online feature selection is proposed. Firstly, the algorithm converted the input image into sixteen different color feature spaces, and fisher discriminating rule was adopted to select the top-ranked feature space which could discriminate the target region and the neighbor background region best. Then, particle filter algorithm based on statistical histogram was applied to track object in the top-ranked feature space. Experimental results show that this algorithm is robust and the target is tracked accurately under the conditions of illumination variation, shape change of target and partial occlusion.