This paper presents a novel object tracking method based on approximated Locality-constrained Linear Coding(LLC). Rather than using a nonnegative constraint on encoding coefficients to guarantee these elements nonnegative, in this paper, the nonnegative constraint is substituted by the a conventional l2 norm regularization term in approximated LLC to obtain the similar nonnegative effect. And we provide a detailed and adequate explanation in theoretical analysis to clarify the rationality of this replacement. Instead of specifying a fixed K nearest neighbors to form the local dictionary, a series of different dictionaries with pre-defined numbers of nearest neighbors are selected. Weights of these various dictionaries are also learned from approximated LLC. In order to alleviate tracking drifts, we propose a simple and efficient occlusion detection method. The occlusion detection criterion mainly depends on whether negative templates are selected to represent the severe occluded target. Both qualitative and quantitative evaluations on several challenging sequences show that the proposed tracking algorithm achieves favorable performance compared with other state-of-the-art methods.

本文提出一种基于近似局部线性编码的物体跟踪方法，通过替代传统的非负约束，使用 L2 正则化项获得相似的非负效果，在理论分析及定位字典的方法上做了较为详细的解释，并在其基础上提出了一种简单高效的遮挡检测方法，实验结果表明该跟踪算法在多个数据集上性能优越。

基于非负正则化多重局部编码的视觉跟踪