Tensor pooling for online visual tracking

Recently, local sparse representation (LSR) has been successfully applied in visual tracking, owing to its discriminative nature and robustness against local noise and occlusions. It is note worthy that local sparse codes computed with a template form a 3-order tensor of their original layout, although most pooling operators convert it to a vector by concatenating or computing statistics on it. As compared to pooling vectors, tensor form could deliver more informative and structured representation for target appearance, and can also avoid high dimensionality learning problem suffered in concatenating pooling based methods. Motivated by above ideas, in this paper, we propose to represent target templates directly with sparse coding tensors, and build the appearance model by incrementally learning on these tensors. We further propose a discriminative framework to improve robustness against drifting and environment noise. Experiments on a recent comprehensive benchmark indicate that our method outperforms state-of-the-art trackers.