Illumination Invariant Object Tracking with Adaptive Sparse Representation

Since the introduction of the sparse representation-based tracking method named l(1), tracker, there have been further studies into this tracking framework with promised results in challenging video sequences. However, in the situation of large illumination changes and shadow casting, the tracked object cannot be modeled efficiently by sparse representation templates. To overcome this problem, we propose a new illumination invariant tracker based on photometric normalization techniques and the sparse representation framework. With photometric normalization methods, we designed a new illumination invariant template presentation for tracking that eliminates the illumination influences, such as brightness variation and shadow casting. For a higher tracking accuracy, we introduced a strategy that adaptively selects the optimum template presentation at the update step of the tracking process. The experiments show that our approach outperforms the previous l(1) and some state-of-the-art algorithms in tracking sequences with severe illumination effects.