Visual tracking with sparse correlation filters

Correlation filters have recently made significant improvements in visual object tracking on both efficiency and accuracy. In this paper, we propose a sparse correlation filter, which combines the effectiveness of sparse representation and the computational efficiency of correlation filters. The sparse representation is achieved through solving an ℓ0 regularized least squares problem. The obtained sparse correlation filters are able to represent the essential information of the tracked target while being insensitive to noise. During tracking, the appearance of the target is modeled by a sparse correlation filter, and the filter is re-trained after tracking on each frame to adapt to the appearance changes of the target. The experimental results on the CVPR2013 Online Object Tracking Benchmark (OOTB) show the effectiveness of our sparse correlation filter-based tracker.