Visual tracking using IPCA and sparse representation

The main challenging issues in visual tracking can be listed as follows: significant variation of object’s appearance or background image, illumination changes, serious or even complete occlusion of object, etc. In order to deal with them, two modules are needed: one is an accurate appearance model updating online and the other one is a robust matching method to find the target according to the learned model. In this paper, we propose a novel tracking method in a particle filter framework based on IPCA and sparse representation, in which IPCA is used to model the object appearance adaptively and sparse representation is used in two aspects: first, it helps to formulate a robust updating scheme of the IPCA; second, it strengthens the matching process significantly when the tracker copes with very challenging cases as mentioned in the beginning. In experiments, we select three state-of-the-art tracking methods for comparison and demonstrate the superiority of our method over them on various data.