A novel reverse sparse model utilizing the spatio-temporal relationship of target templates for object tracking.

Highlights • A weighted reverse joint sparse model, considering the spatio-temporal characteristic of multiple templates, is proposed. • A binary indicator vector is employed to suppress the inaccurate results generated by the corrupted local image blocks. • An efficient solver, which is based on the smooth l 0, 2 norm, is proposed to solve the weighted joint sparse model. • The experimental results on OTB-100 show the proposed method outperforms other state-of-the-art tracking methods. Abstract In the particle filter framework, the sparse representation method models object tracking by predicting the likelihood of particles with respect to target templates. However, in most tracking methods, the spatio-temporal relationships of different templates have been neglected. This method applies the reverse sparse representation in multi-task learning, thus takes advantage of the temporal information and spatial continuity of a series of templates. Adding a predesigned weight matrix can reflect the temporal information, and make the new entrant templates have a greater influence on atoms' selection. To solve the locally weighted reverse joint sparse model (LWRJM), we design a modified smooth l 0, 2 algorithm which requires only a few iterations. The obtained sparse coding coefficients are mapped to a binary indicator vector by a statistic strategy, which is helpful to eliminate the contributions of the corrupted blocks and preserve the uncorrupted ones. Comparing with other state-of-the-art tracking methods on 100 challenging benchmark image sequences, the proposed tracker (LWRJM) outperforms other methods in both qualitative and quantitative evaluations. [ABSTRACT FROM AUTHOR]