Robust Visual Tracking via Discriminative Structural Sparse Feature

In this paper, we propose a robust visual tracking method by exploiting both the structural and the context information. Firstly we take use of the sparse coding's robust to occlusion and illumination and extract the structural local sparse feature, upon which we create a discriminative model between the target and the context. Then we introduce an adaptive online SVM algorithm to searching the feature space and discriminate the target from the context patches. Furthermore, the update of the dictionary and the SVM model consider both the latest observations and the original template, thereby enabling the tracker to deal with appearance change and alleviate the drift problem. Experiments compared with the state of art algorithm demonstrate that the proposed tracker performs excellent in the challenging videos.