Visual Tracking by Sampling Tree-Structured Graphical Models

  • Seunghoon Hong
  • Bohyung Han
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8689)


Probabilistic tracking algorithms typically rely on graphical models based on the first-order Markov assumption. Although such linear structure models are simple and reasonable, it is not appropriate for persistent tracking since temporal failures by short-term occlusion, shot changes, and appearance changes may impair the remaining frames significantly. More general graphical models may be useful to exploit the intrinsic structure of input video and improve tracking performance. Hence, we propose a novel offline tracking algorithm by identifying a tree-structured graphical model, where we formulate a unified framework to optimize tree structure and track a target in a principled way, based on MCMC sampling. To reduce computational cost, we also introduce a technique to find the optimal tree for a small number of key frames first and employ a semi-supervised manifold alignment technique of tree construction for all frames. We evaluated our algorithm in many challenging videos and obtained outstanding results compared to the state-of-the-art techniques quantitatively and qualitatively.


Visual tracking tree-structured graphical model Markov Chain Monte Carlo (MCMC) manifold alignment 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Seunghoon Hong
    • 1
  • Bohyung Han
    • 1
  1. 1.Department of Computer Science and EngineeringPOSTECHKorea

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