Visual Tracking via Detecting and Removing Outliers Based on Block Sparse Representation

In visual tracking, subspace representation has been proven effective in many cases. However, in some scenarios the target observations may contain outliers (e.g. pixels that are occluded) for which subspace representation does not hold. In order to deal with this issue and noticing that outliers usually occur in clusters and occupy only a fraction of the whole target image patch, we describe them by a block sparse vector and add it to the original subspace model. Under the framework of variational Bayesian inference, we then develop an algorithm to recover the sparse vector. With the knowledge of such a vector, the outliers can be detected and removed and subspace representation can be conducted over the outlier-free observations. Based on this strategy, the proposed tracking method needs to perform sparse recovery only once for each frame, which brings significant computational savings compared to existing tracking methods that exploit sparse representation. Moreover, unlike the existing methods that typically employ the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm regularization approach to achieve sparse recovery and have to set the regularization parameter appropriately, the proposed method can estimate the unknown coefficient vectors and all other unknown model parameters in an automatic manner and thus requires less user intervention in the tracking process. Experimental results on various videos demonstrate that the method can achieve good tracking performance at a low computational cost.