Robust object tracking via incremental subspace dynamic sparse model

Sparse representation has been widely applied to some generative tracking methods. However, these methods do not consider the correlation between sparse representation coefficients in the time domain. In this paper, we propose a novel incremental subspace dynamic sparse tracking (ISDST) model with the error term of Gaussian-Laplacian distribution, which fully considers the correlation of object representations between consecutive frames by compressive sensing, and can effectively handle the occlusion in scenes. Next, the outlier entries, especially caused by the occlusion, have some group effect, so we adopt the spatial structured sparse via l1, 2 mixed norms instead of the original l1 sparse items. In addition, since the occlusion changes is very little between consecutive frames, we maintain an occlusion mask and eliminate the influence of occlusion pixels in the process of calculating the likelihood probability. Extensive experiments on challenging sequences demonstrate that our method consistently outperforms existing state-of-the-art methods.