Visual Tracking by Sampling Tree-Structured Graphical Models

Seunghoon Hong and Bohyung Han

Abstract. 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.

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

LNCS 8689, p. 1 ff.

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