Dual-scale structural local sparse appearance model for robust object tracking

Recently, sparse representation has been applied in object tracking successfully. However, the existing sparse representation captures either the holistic features of the target or the local features of the target. In this paper, we propose a dual-scale structural local sparse appearance (DSLSA) model based on overlapped patches, which can capture the quasi-holistic features and the local features of the target simultaneously. This paper first proposes two-scales structural local sparse appearance models based on overlapped patches. The larger-scale model is used to capture the structural quasi-holistic feature of the target, and the smaller-scale model is used to capture the structural local features of the target. Then, we propose a new mechanism to associate these two scale models as a new dual-scale appearance model. Both qualitative and quantitative analyses on challenging benchmark image sequences indicate that the tracker with our DSLSA model performs favorably against several state-of-the-art trackers.