Robust tracking via saliency-based appearance model

Bo Luo, Ruimin Hu, Yimin Wang, Chao Liang, Chunjie Zhang, Ge Gao

Abstract: We propose a novel local-based saliency measure (LBSM) method for object tracking problem. In LBSM method, salient patches are defined as the patches having great local changes. Then we apply the saliency information derived from LBSM to appearance model by giving weights to patches according to their saliency levels. The patches with higher saliency levels are given larger weights. As a result, the appearance model is improved owing to the use of saliency information. Extensive experiments conducted on various challenging sequences demonstrate the effectiveness of LB-SM in tracking procedure, and our saliency-based tracker performs well against state-of-the-art algorithms.