Spatial Neighborhood-Constrained Linear Coding for Visual Object Tracking

In this paper, a new spatial neighborhood-constrained linear coding strategy which realizes sparse representation is proposed for visual object tracking. Unlike conventional sparse and locality-constrained linear coding approaches that need an extra post-processing stage to incorporate the spatial layout information, the proposed coding strategy intrinsically embeds the spatial layout information into the coding stage. The proposed coding strategy can also be used to effectively realize joint sparse representation for different feature descriptors. In addition, based on the distance to the “ideal point” in the reconstruction error space, a new multicue integration approach for robust tracking is proposed and a co-learning approach is developed to update the dictionaries. Finally, the proposed tracking algorithm is compared with other state-of-the-art trackers on some challenging video sequences and shows promising results.