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Recognizing Actions across Cameras by Exploring the Correlated Subspace

  • Chun-Hao Huang
  • Yi-Ren Yeh
  • Yu-Chiang Frank Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7583)

Abstract

We present a novel transfer learning approach to cross-camera action recognition. Inspired by canonical correlation analysis (CCA), we first extract the spatio-temporal visual words from videos captured at different views, and derive a correlation subspace as a joint representation for different bag-of-words models at different views. Different from prior CCA-based approaches which simply train standard classifiers such as SVM in the resulting subspace, we explore the domain transfer ability of CCA in the correlation subspace, in which each dimension has a different capability in correlating source and target data. In our work, we propose a novel SVM with a correlation regularizer which incorporates such ability into the design of the SVM. Experiments on the IXMAS dataset verify the effectiveness of our method, which is shown to outperform state-of-the-art transfer learning approaches without taking such domain transfer ability into consideration.

Keywords

Support Vector Machine Action Recognition Canonical Correlation Analysis Target Domain Transfer Learning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Chun-Hao Huang
    • 1
  • Yi-Ren Yeh
    • 1
  • Yu-Chiang Frank Wang
    • 1
  1. 1.Research Center for IT Innovation, Academia SinicaTaipeiTaiwan

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