关键词: 目标跟踪, 粒子滤波, 逆稀疏表示, 局部余弦相似度

Abstract: In order to address local occlusion and deformation of target tracking, an inverse sparse visual target tracking strategy with local cosine similarity training weights is proposed in this paper. According to the core idea of particle filter in parameter space, based on the framework of inverse sparse, the inverse sparse representation coefficients of candidates are obtained by using the candidates to reconstruct template, and the best candidate which is more similar with template is selected according to the feature of coefficient distribution. In order to overcome the influence of occlusion, a new objective function is introduced to improve the discrimination of the local block in template：calculate the local cosine similarity difference between the positive samples and negative samples and the template, and then the function of quadratic optimization is used to update weights with ability of discrimination. In order to avoid invalidation of template updating, this paper updates template based on the information of weights selectively. Experimental results demonstrate that the proposed algorithm is robust on challenging benchmark video sequences with complicated conditions including occlusion and so on, has its own advantages compared with other existing algorithms.

Key words: visual tracking, particle filter, inverse sparse representation, local cosine similarity