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http://dx.doi.org/10.3837/tiis.2019.02.021

Object Tracking Based on Weighted Local Sub-space Reconstruction Error  

Zeng, Xianyou (Institute of Information Science, Beijing Jiaotong University)
Xu, Long (Key Laboratory of Solar Activity, National Astronomical Observatories, Chinese Academy of Sciences)
Hu, Shaohai (Institute of Information Science, Beijing Jiaotong University)
Zhao, Ruizhen (Institute of Information Science, Beijing Jiaotong University)
Feng, Wanli (Institute of Information Science, Beijing Jiaotong University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.13, no.2, 2019 , pp. 871-891 More about this Journal
Abstract
Visual tracking is a challenging task that needs learning an effective model to handle the changes of target appearance caused by factors such as pose variation, illumination change, occlusion and motion blur. In this paper, a novel tracking algorithm based on weighted local sub-space reconstruction error is presented. First, accounting for the appearance changes in the tracking process, a generative weight calculation method based on structural reconstruction error is proposed. Furthermore, a template update scheme of occlusion-aware is introduced, in which we reconstruct a new template instead of simply exploiting the best observation for template update. The effectiveness and feasibility of the proposed algorithm are verified by comparing it with some state-of-the-art algorithms quantitatively and qualitatively.
Keywords
visual tracking; sub-space reconstruction error; generative weights; template update;
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