Robust object tracking based on sparse representation and incremental weighted PCA

Abstract

Object tracking plays a crucial role in many applications of computer vision, but it is still a challenging problem due to the variations of illumination, shape deformation and occlusion. A new robust tracking method based on incremental weighted PCA and sparse representation is proposed. An iterative process consisting of a soft segmentation step and a foreground distribution update step is adpoted to estimate the foreground distribution, cooperating with incremental weighted PCA, we can get the target appearance in terms of the PCA components with less impact of the background in the target templates. In order to make the target appearance model more discriminative, trivial and background templates are both added to the dictionary for sparse representation of the target appearance. Experiments show that the proposed method with some level of background awareness is robust against illumination change, occlusion and appearance variation, and outperforms several latest important tracking methods in terms of tracking performance.