ROBUST VISUAL TRACKING VIA ADAPTIVE STRUCTURE-ENHANCED PARTICLE FILTER

An effective representation model plays an important role in the visual tracking, as it relates to how the most meaningful information are recognized and understood in the dictionary space. However, it is difficult to know the structure and the weights of tracking objects in advance. In addition, how to balance the adaption and robustness in tracking algorithms remains a nontrivial problem. In this paper, we propose a robust visual tracker based on adaptive structure-enhanced regularizations, and achieve a sequential Monte Carlo searching via simplified particle filters. Specifically, multiple atomic norms are incorporated in the cost function in the target dictionary space, and their weights are updated adaptively during the detection step between each frame. Sparse and low-rank structures as well as other atomic norms enhance the robustness by capturing various features meanwhile ruling out outliers, and the velocity of moving objects are considered accordingly in the probabilistic distribution of particles. Moreover, the algorithm has been accelerated by adopting prefilters as classifiers for target particles using pixel variances in colours and intensities, which ensures a real-time tracking in practice. On challenging tracking datasets, the proposed approach show advantages in tracking fast-moving objects and favorable performance against other 10 state-of-the-art visual trackers.