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?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. HighlightsWe sparsely represent the object in both global and local level for tracking, which aim to explore the object?s holistic and local information respectively.The global dictionary and classifier are coupled learned in our global part.We define temporal and spatial consistencies among the object patches, and refine the tracking result by ensuring the consistencies.