Block covariance based l1 tracker with a subtle template dictionary

Sparse representation is one of the most influential frameworks for visual tracking. However, when applying this framework to the real-world tracking applications, there are still many challenges such as appearance variations and background noise. In this paper, we propose a new l"1-regularized sparse representation based tracking algorithm. The contributions of our work are: (1) A block-division based covariance feature is incorporated into the sparse representation framework. This feature has two advantages-(a) the feature is more discriminative than the original image patch and (b) the block information is robust for occlusion reasoning. (2) A subtle template dictionary is constructed including a fixed template, a stable template and other variational templates; and these templates are selectively updated to capture the appearance variations and prevent the model from drifting. (3) The sparse representation framework is extended to multi-object tracking, where the multi-object tracking task can be easily decentralized to a set of individual trackers. Experimental results demonstrate that, compared with several state-of-the-art tracking algorithms, the proposed algorithm is more robust and effective.