Visual Tracking Based on Sparse Dense Structure Representation and Online Robust Dictionary Learning

The L1 trackers are robust to moderate occlusion. However, the L1 trackers are very computationally expensive and prone to model drift. To deal with these problems, firstly, a robust representation model is proposed based on sparse dense structure. The tracking robustness is improved by adding an L2 norm regularization on the coefficients associated with the target templates and L1 norm regularization on the coefficients associated with the trivial templates. To accelerate object tracking, a block coordinate optimization theory based fast numerical algorithm for the proposed representation model is designed via the ridge regression and the soft shrinkage operator. Secondly, to avoid model drift, an online robust dictionary learning algorithm is proposed for template update. Robust fast visual tracker is achieved via the proposed representation model and dictionary learning algorithm in particle filter framework. The experimental results on several challenging image sequences show that the proposed method has better performance than the state-of-the-art tracker.