Robust Visual Tracking via Multiple Kernel Boosting with Affinity Constraints

Fan Yang, Huchuan Lu, Ming Hsuan Yang

Research output: Contribution to journalArticlepeer-review

55 Citations (Scopus)


We propose a novel algorithm by extending the multiple kernel learning framework with boosting for an optimal combination of features and kernels, thereby facilitating robust visual tracking in complex scenes effectively and efficiently. While spatial information has been taken into account in conventional multiple kernel learning algorithms, we impose novel affinity constraints to exploit the locality of support vectors from a different view. In contrast to existing methods in the literature, the proposed algorithm is formulated in a probabilistic framework that can be computed efficiently. Numerous experiments on challenging data sets with comparisons to state-of-the-art algorithms demonstrate the merits of the proposed algorithm using multiple kernel boosting and affinity constraints.

Original languageEnglish
Article number6572853
Pages (from-to)242-254
Number of pages13
JournalIEEE Transactions on Circuits and Systems for Video Technology
Issue number2
Publication statusPublished - 2014 Feb

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

All Science Journal Classification (ASJC) codes

  • Media Technology
  • Electrical and Electronic Engineering


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