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Indexed by:期刊论文

Date of Publication:2016-09-01

Journal:IEEE TRANSACTIONS ON CYBERNETICS

Included Journals:SCIE、EI、Scopus

Volume:46

Issue:9

Page Number:2144-2155

ISSN No.:2168-2267

Key Words:Absorbing Markov chain; ergodic Markov chain; random walks; visual tracking

Abstract:In this paper, we formulate visual tracking as random walks on graph models with nodes representing superpixels and edges denoting relationships between superpixels. We integrate two novel graphs with the theory of Markov random walks, resulting in two Markov chains. First, an ergodic Markov chain is enforced to globally search for the candidate nodes with similar features to the template nodes. Second, an absorbing Markov chain is utilized to model the temporal coherence between consecutive frames. The final confidence map is generated by a structural model which combines both appearance similarity measurement derived by the random walks and internal spatial layout demonstrated by different target parts. The effectiveness of the proposed Markov chains as well as the structural model is evaluated both qualitatively and quantitatively. Experimental results on challenging sequences show that the proposed tracking algorithm performs favorably against state-of-the-art methods.