Grayscale-thermal Tracking via Canonical Correlation Analysis Based Inverse Sparse Representation

The grayscale-thermal tracking has attracted increasing attention due to the fact that it can make thermal information complement with grayscale information. Since there exists a large gap between the grayscale and the thermal video sequences, how to exploit the intrinsic relation between the grayscale and the thermal targets has become the key point. To address this issue, in this paper, we propose an inverse sparse representation based framework for the grayscale-thermal tracking, in which a canonical correlation analysis based inverse sparse representation model is adopted to jointly encode the target candidates in the grayscale and the thermal video sequences. The target coding process can explore the similarity between the grayscale and the thermal appearance in a common subspace, which can highlight the useful and discriminative information in both grayscale and thermal targets. The experiments on OSU-CT dataset can illustrate the promising performance of our tracking framework.