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

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 10, 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. (C) 2018 Elsevier B.V. All rights reserved.