A Particle Swarm Optimization Algorithm with Local Sparse Representation for Visual Tracking

Abstract

Handling appearance variations caused by the occlusion or abrupt motion is a challenging task for visual tracking. In this paper, we propose a novel tracking method that deals with the appearance changes based on sparse representation in a particle swarm optimization (PSO) framework. First, we divide each candidate state into multiple structural patches to cope with the partial occlusions of the object. Once the object is lost, we present an object's recovery scheme by the scale invariant feature transforms (SIFT) correspondence between two frames to reacquire the rough object position. Then the tracking state is searched in the vicinage of the rough object position using the PSO iteration. In addition, an online dictionary updating mechanism is presented to capture the object appearance variations. The object information from the initial frame is never updated in the tracking, while other templates in the dictionary are progressively updated based on the coefficients of templates. Compared with several conventional trackers, the experimental results demonstrate that our approach is more robust in dealing with the occlusions and abrupt motion variations.