Collaborating Frames: Temporally Weighted Sparse Representation for Visual Tracking
Sparse representation techniques for visual tracking have rarely taken advantage of the similarity between target objects in consecutive frames. In this paper, the target is divided into disjoint patches, and the sparse representation of corresponding consecutive target patches is assumed to be distributed according to a common Laplacian Scale Mixture (LSM) with a shared scale parameter. The target patches collaborate to determine this shared parameter, which in turn encourages smooth temporal variation in their representations. The target’s appearance is modeled using a dictionary composed of patch templates. This patchwise treatment allows occluded patches to be detected and excluded when updating the dictionary. Experimental results on 6 challenging video sequences, show superior performance, especially in scenarios with considerable appearance change.
