Human tracking by multiple kernel boosting with locality affinity constraints

Authors:
Fan Yang;Huchuan Lu;Yen-Wei Chen
Affiliations:
School of Information and Communication Engineering, Dalian University of Technology, Dalian, China;School of Information and Communication Engineering, Dalian University of Technology, Dalian, China;School of Information and Communication Engineering, Dalian University of Technology, Dalian, China and College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan
Venue:
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Year:
2010

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Abstract

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.