Sparse Feature Learning for Visual Tracking by Least Absolute Shrinkage and Selection Operator

In this paper, a robust visual tracking method is proposed by using Least Absolute Shrinkage and Selection Operator (Lasso) in a particle filter framework. First, to locate the tracking target at a new frame, each target candidate is sparsely represented in the space spanned by sampling particle images and sparsity vector. The lasso can replace the sparse approximation problem by a convex problem. Then, the likelihood is evaluated by the sparsity coefficients which is very different from the current tracking scheme using sparse representation. The combination of sampling images with coefficients bigger than a threshold will be taken as the tracking target without any template. After that, tracking is continued using a Bayesian state inference framework in which a particle filter is used for propagating sample distributions over time. The dynamic target updating scheme keeps track of the most representative particles throughout the tracking procedure. The proposed approach shows excellent performance on several image sequences.