Structured Visual Tracking with Dynamic Graph

  • Zhaowei Cai
  • Longyin Wen
  • Jianwei Yang
  • Zhen Lei
  • Stan Z. Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7726)


Structure information has been increasingly incorporated into computer vision field, whereas only a few tracking methods have employed the inner structure of the target. In this paper, we introduce a dynamic graph with pairwise Markov property to model the structure information between the inner parts of the target. The target tracking is viewed as tracking a dynamic undirected graph whose nodes are the target parts and edges are the interactions between parts. These target parts within the graph waiting for matching are separated from the background with graph cut, and a spectral matching technique is exploited to accomplish the graph tracking. With the help of an intuitive updating mechanism, our dynamic graph can robustly adapt to the variations of target structure. Experimental results demonstrate that our structured tracker outperforms several state-of-the-art trackers in occlusion and structure deformations.


Color Histogram Visual Tracking Conditional Random Field Graph Match Dynamic Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Zhaowei Cai
    • 1
  • Longyin Wen
    • 1
  • Jianwei Yang
    • 1
  • Zhen Lei
    • 1
  • Stan Z. Li
    • 1
    • 2
  1. 1.CBSR & NLPR, Institute of AutomationChinese Academy of SciencesChina
  2. 2.China Research and Development Center for Internet of ThingChinese Academy of SciencesChina

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