中文摘要：

传统的稀疏表示跟踪算法直接利用较为简单的灰度特征进行稀疏表示系数计算，易受遮挡、形变等影响。为此，提出一种局部自适应加权算法来增加受遮挡、形变等影响的候选目标与未受遮挡、形变等影响的候选目标之间的区分度。另外，一般稀疏表示算法利用数量较少的目标模板构建过完备字典。无法获得较好的稀疏系数。提出逆稀疏表示算法，利用包含丰富目标特征和背景特征的候选目标构建过完备字典来重构目标模板，相同维度的目标模板条件下可以获得更好的稀疏系数。实验表明，该算法在目标背景差异小或严重遮挡、形变情况下，都能够较好的跟踪目标。

英文摘要：

Traditional sparse representation tracker use simple grayscale characteristics in calculating sparse coefficient, which is easily affected by the heavy occlusions and deformation. To this end, a local adaptive weighting algorithm is put forward to increase degree of differentiation between the candidate targets affected by shade, deformation, etc and not affected by the shade, deformation, etc. In addition, the general sparse representation algorithm use a small number of target templates to build a complete dictionary, which unable to get a better sparse coefficient. Inverse structure sparse representation algorithm, using the candidate target which contains rich target and background features to build a complete dictionary to reconstruct the target template under the condition of the same dimension target template better sparse coefficient can be obtained, is proposed. Experiments show that the proposed algorithm in the small differences between target and background or serious barrier, deformation, can better track the target.