A New Template Update Scheme for Visual Tracking

Xiaohuan Lu, Shuangyan Yi, Zhenyu He, Hongpeng Wang, Wensheng Chen

Abstract: Single object tracking can be focused on two phases under the particle filter framework: one is sparse representation, which can be regarded as a matching evaluation; the other is template update, which can be regarded as the appearance changes of the target. Template update is the most direct and basic phase to ensure a high quality tracking. However, most template update schemes can not capture the latest appearance of the target, thereby leading to a low quality tracking. In this paper, we propose a new template update scheme, which can obtain the latest trends of the target. The experimental results on popular benchmark video sequences show that the proposed template update scheme is feasible and effective.