Global coupled learning and local consistencies ensuring for sparse-based tracking

This paper presents a robust tracking algorithm by sparsely representing the object at both global and local levels. Accordingly, the algorithm is constructed by two complementary parts: Global Coupled Learning (GCL) part and Local Consistencies Ensuring (LCE) part. The global part is a discriminative model which aims to utilize the holistic features of the object via an over-complete global dictionary and classifier, and the dictionary and classifier are coupled learning to construct an adaptive GCL part. While in LCE part, we explore the object[U+05F3]s local features by sparsely coding the object patches via a local dictionary, then both temporal and spatial consistencies of the local patches are ensured to refine the tracking results. Moreover, the GCL and LCE parts are integrated into a Bayesian framework for constructing the final tracker. Experiments on fifteen benchmark challenging sequences demonstrate that the proposed algorithm has more effectiveness and robustness than the alternative ten state-of-the-art trackers.