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Action recognition using 3D DAISY descriptor

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Abstract

In this paper we propose a novel spatial-temporal descriptor for action recognition. We extend a recent image local descriptor, DAISY, to three dimensions to deal with the information in the additional temporal domain in videos. The new 3D DAISY descriptor is both functionally discriminative and computationally efficient. We use the bag-of-words framework and non-linear SVM for classification. The experiments on public action datasets, KTH, WEIZMANN, YouTube, and UT-Interaction, demonstrate the promising results of our method.

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Acknowledgments

This work was supported by National Natural Science Foundation of China (61332012), National Basic Research Program of China (2013CB329305), 100 Talents Programme of The Chinese Academy of Sciences, and Strategic Priority Research Program of the Chinese Academy of Sciences (\(XDA06030601\)).

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Authors and Affiliations

  1. School of Computer Science and Technology, Tianjin University, Tianjin, 300072, China

    Xiaochun Cao, Hua Zhang & Chao Deng

  2. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China

    Xiaochun Cao

  3. University of Rochester, Rochester, NY, 14620, USA

    Qiguang Liu

  4. State University of New York, Stony Brook, NY, 11790, USA

    Hanyu Liu

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  1. Xiaochun Cao
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  2. Hua Zhang
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  3. Chao Deng
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  4. Qiguang Liu
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  5. Hanyu Liu
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Correspondence to Xiaochun Cao.

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Cao, X., Zhang, H., Deng, C. et al. Action recognition using 3D DAISY descriptor. Machine Vision and Applications 25, 159–171 (2014). https://doi.org/10.1007/s00138-013-0545-6

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  • DOI: https://doi.org/10.1007/s00138-013-0545-6

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