Robust Object Tracking Based on Principal Component Analysis and Local Sparse Representation

Abstract

Object tracking methods based on the principal component analysis (PCA) are effective against object change caused by illumination variation and motion blur. However, when the object is occluded, the tracking result of the PCA-based methods will drift away from the target. In this paper, we propose a new robust object tracking method based on the PCA and local sparse representation (LSR). First, candidates are reconstructed through the PCA subspace model in global manner. To handle occlusion, a patch-based similarity estimation strategy is proposed for the PCA subspace model. In the patch-based strategy, the PCA representation error map is divided into patches to estimate the similarity between target and candidate considering the occlusion. Second, the LSR is introduced to detect the occluded patches of the object and estimate the similarity through the residual error in the sparse coding. Finally, the two similarities of each candidate from the PCA subspace model and LSR model are fused to predict the tracking result. The experimental results demonstrate that the proposed tracking method favorably performs against several state-of-the-art methods on challenging image sequences.