Robust visual tracking via incremental maximum margin criterion

Authors:
Lu Wang;Ming Wen;Chong Wang;Wenyuan Wang
Affiliations:
Department of Automation, Tsinghua University, Beijing, P.R. China;Department of Automation, Tsinghua University, Beijing, P.R. China;Department of Automation, Tsinghua University, Beijing, P.R. China;Department of Automation, Tsinghua University, Beijing, P.R. China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

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Abstract

Robust visual object tracking is one of the key problems in computer vision. Subspace based tracking method is a promising approach in handling appearance variability. Linear Discriminant Analysis(LDA) has been applied to this problem, but LDA is not a stable algorithm especially for visual tracking. Maximum Margin Criterion(MMC) is a recently proposed discriminant criterion. Its promising specialities make it a better choice for the tracking problem. In this paper, we present a novel subspace tracking algorithm based on MMC. We also proposed an incremental version of the corresponding algorithm so that the tracker can update in realtime. Experiments show our tracking algorithm is able to track objects well under large lighting, pose and expression variation.