Robust visual tracking via efficient manifold ranking with low-dimensional compressive features

Abstract In this paper, a novel and robust tracking method based on efficient manifold ranking is proposed. For tracking, tracked results are taken as labeled nodes while candidate samples are taken as unlabeled nodes. The goal of tracking is to search the unlabeled sample that is the most relevant to the existing labeled nodes. Therefore, visual tracking is regarded as a ranking problem in which the relevance between an object appearance model and candidate samples is predicted by the manifold ranking algorithm. Due to the outstanding ability of the manifold ranking algorithm in discovering the underlying geometrical structure of a given image database, our tracker is more robust to overcome tracking drift. Meanwhile, we adopt non-adaptive random projections to preserve the structure of original image space, and a very sparse measurement matrix is used to efficiently extract low-dimensional compressive features for object representation. Furthermore, spatial context is used to improve the robustness to appearance variations. Experimental results on some challenging video sequences show that the proposed algorithm outperforms seven state-of-the-art methods in terms of accuracy and robustness.