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Subspace Learning for Action Recognition

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Human Activity Recognition and Prediction

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Abstract

Recently human action recognition [4, 5, 8–10] has aroused widely attention for public surveillance system, elder service system, etc. However, the data captured by webcams are often high dimensional and usually contain noise and redundancy. So it is crucial to extract the meaning information by mitigating uncertainties for higher accuracy of recognition task. From this motivation, there are two topics we propose in this chapter: (1) select key frames from a video to remove noise and redundancy and (2) learn a subspace for dimensional reduction to reduce time complexity.

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Notes

  1. 1.

    http://www.grouplens.org/node/73.

  2. 2.

    http://archive.ics.uci.edu/ml/datasets.html.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Northeastern University, 360 Huntington Avenue, Boston, MA, 02115, USA

    Chengcheng Jia

  2. Department of Electrical and Computer Engineering and College of Computer and Information Science (Affiliated), Northeastern University, 360 Huntington Avenue, Boston, MA, 02115, USA

    Yun Fu

Authors
  1. Chengcheng Jia
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  2. Yun Fu
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Correspondence to Chengcheng Jia .

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Editors and Affiliations

  1. Northeastern University, Boston, Massachusetts, USA

    Yun Fu

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Jia, C., Fu, Y. (2016). Subspace Learning for Action Recognition. In: Fu, Y. (eds) Human Activity Recognition and Prediction. Springer, Cham. https://doi.org/10.1007/978-3-319-27004-3_3

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  • DOI: https://doi.org/10.1007/978-3-319-27004-3_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27002-9

  • Online ISBN: 978-3-319-27004-3

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