Human Tracking by Multiple Kernel Boosting with Locality Affinity Constraints

  • Fan Yang
  • Huchuan Lu
  • Yen-Wei Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6495)


In this paper, we incorporate the concept of Multiple Kernel Learning (MKL) algorithm, which is used in object categorization, into human tracking field. For efficiency, we devise an algorithm called Multiple Kernel Boosting (MKB), instead of directly adopting MKL. MKB aims to find an optimal combination of many single kernel SVMs focusing on different features and kernels by boosting technique. Besides, we apply Locality Affinity Constraints (LAC) to each selected SVM. LAC is computed from the distribution of support vectors of respective SVM, recording the underlying locality of training data. An update scheme to reselect good SVMs, adjust their weights and recalculate LAC is also included. Experiments on standard and our own testing sequences show that our MKB tracking outperforms some other state-of-the-art algorithms in handling various conditions.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Fan Yang
    • 1
  • Huchuan Lu
    • 1
  • Yen-Wei Chen
    • 1
    • 2
  1. 1.School of Information and Communication EngineeringDalian University of TechnologyDalianChina
  2. 2.College of Information Science and EngineeringRitsumeikan UniversityKusatsuJapan

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