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A Novel Group-Sparsity-Optimization-Based Feature Selection Model for Complex Interaction Recognition

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Computer Vision -- ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9007))

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Abstract

Interaction recognition is an important part of action recognition and has various applications such as surveillance systems, human computer interface, and machine intelligence. In this paper, we propose a novel group-sparsity-optimization-based feature selection model for complex interaction recognition. Firstly multiple local and global features are concatenated into a feature pool, and then based on the group sparsity optimization, different feature types are automatically selected to fit specific interaction categorization. We test our method on the benchmark dataset: the UT-interaction dataset. Experimental results substantiate the effectiveness of the proposed method on complex interaction recognition tasks as compared with current state-of-the-art methods.

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Acknowledgement

This work is supported by the National Natural Science Foundation of China (No. 61102131, 61373114, 61275099), the Natural Science Foundation of Chongqing Science and Technology Commission (No. cstc2014jcyjA40048), the Project of Key Laboratory of Signal and Information Processing of Chongqing (No. CSTC2009CA2003).

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Authors and Affiliations

  1. Chongqing Key Laboratory of Signal and Information Processing, Chongqing University of Posts and Telecommunications, Chongqing, China

    Luyu Yang & Chenqiang Gao

  2. School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China

    Deyu Meng

  3. School of Computer Science, Carnegie Mellon University, Pittsburgh, USA

    Lu Jiang

Authors
  1. Luyu Yang
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  2. Chenqiang Gao
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  3. Deyu Meng
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  4. Lu Jiang
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Corresponding author

Correspondence to Chenqiang Gao .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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Yang, L., Gao, C., Meng, D., Jiang, L. (2015). A Novel Group-Sparsity-Optimization-Based Feature Selection Model for Complex Interaction Recognition. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9007. Springer, Cham. https://doi.org/10.1007/978-3-319-16814-2_33

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  • DOI: https://doi.org/10.1007/978-3-319-16814-2_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16813-5

  • Online ISBN: 978-3-319-16814-2

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