Robust visual tracking via discriminative sparse point matching

This paper present a discriminative sparse point matching method (DSPM) for tracking generic objects in vision applications. Different from the conventional tracking methods that involves the construction of high-level or self-learning features, DSPM particularly focuses on a optical flow based point matching optimization method for overcoming the variation of object deformation in motion. The algorithm contains two key issues: a stable point matching method based on the global smoothing constraint with optical flow correspondence and a discriminative sparse point selection strategy for distinguishing the object from its surrounding background. Due to the efficient sparse point matching method, the algorithm is able to track objects that undergo fast motion and considerable shape or appearance variations. The proposed tracking method has been thoroughly evaluated on challenging benchmark video sequences and performs a excellent experimental result.