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 l1-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.

► A discriminative block-division based covariance feature is proposed.
► A subtle template dictionary is constructed and selectively updated.
► The sparse representation framework is extended to multi-object tracking.