Learning online structural appearance model for robust object tracking

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• 作者：Min Yang (1)
  MingTao Pei (1)
  YuWei Wu (1)
  YunDe Jia (1)
  
  1. Beijing Laboratory of Intelligent Information Technology ; School of Computer Science ; Beijing Institute of Technology ; Beijing ; 100081 ; China
  
• 关键词：object tracking ; structural appearance model ; sparse representation ; online metric learning ; 鐩爣璺熻釜 ; 缁撴瀯鍖栬〃瑙傛ā鍨?/li> 绋€鐤忚〃绀?/li> 鍦ㄧ嚎搴﹂噺瀛︿範 ; 032106
• 刊名：SCIENCE CHINA Information Sciences
• 出版年：2015
• 出版时间：March 2015
• 年：2015
• 卷：58
• 期：3
• 页码：1-14
• 全文大小：2,169 KB
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• 刊物类别：Computer Science
• 刊物主题：Chinese Library of Science
  Information Systems and Communication Service
  
• 出版者：Science China Press, co-published with Springer
• ISSN：1869-1919

文摘

The main challenge of robust object tracking comes from the difficulty in designing an adaptive appearance model that is able to accommodate appearance variations. Existing tracking algorithms often perform self-updating of the appearance model with examples from recent tracking results to account for appearance changes. However, slight inaccuracy of tracking results can degrade the appearance model. In this paper, we propose a robust tracking method by evaluating an online structural appearance model based on local sparse coding and online metric learning. Our appearance model employs pooling of structural features over the local sparse codes of an object region to obtain a middle-level object representation. Tracking is then formulated by seeking for the most similar candidate within a Bayesian inference framework where the distance metric for similarity measurement is learned in an online manner to match the varying object appearance. Both qualitative and quantitative evaluations on various challenging image sequences demonstrate that the proposed algorithm outperforms the state-of-the-art methods.