目的 为了提高目标跟踪的准确度，提出结合背景信息的自适应加权压缩跟踪算法。方法 针对目标边缘背景信息的干扰问题，提出对目标框架分块提取特征，根据区域分配权值，弱化特征提取过程中背景信息的干扰；利用正负样本特征概率分布的Bhattacharyya距离，自适应地选取区分度较大的特征进行分类器训练，提高分类器的鲁棒性；针对目标遮挡导致分类器分类不准确问题，提出设置目标遮挡检测机制，结合目标和局部背景信息对目标实现遮挡环境下的跟踪。结果 与目前较流行的5种算法在6个具有挑战性的序列中进行比较，本文提出的算法平均跟踪率达到90%，平均每帧耗时0.088 6 s。结论 本文算法在背景干扰，光线变换，目标旋转、形变、遮挡和复杂背景环境下的跟踪具有较高鲁棒性。

Objective A target block feature extraction method is proposed to reduce the interference of background information around the target. Method The features from the blocks in the tracking box are assigned different weights according to their locations to weaken background influence. Features with good discrimination are adaptively selected to train the classifier using the Bhattacharyya distance of the probability distribution of positive and negative samples for improving classifier robustness. The classifier may obtain incorrect information if it continues learning when the tracking object is largely occluded. Thus, a target occlusion detection approach that uses target and local background information is proposed to track successfully when occlusion occurs. Result Compared with five state-of-the-art algorithms on six challenging sequences, the proposed algorithm has an average success rate of 90% and 0.088 6 seconds per frame. Conclusion Experimental results show that the proposed algorithm has good performance and can track successfully and efficiently for many complicated situations, such as swift movement, object deformation, complex background, and occlusion and illumination variation.