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Robust Human Action Recognition Using Dynamic Movement Features

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Intelligent Robotics and Applications (ICIRA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10462))

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Abstract

Action recognition has been widely researched in video surveillance, auxiliary medical care and robotics. In the context of robotics, in order to program robots by demonstration (PbD), we not only need our algorithms to be capable of identifying different actions, but also to be able to encode and reproduce them. Dynamic movement primitives (DMPs), as a trajectory encoding method, are widely used in motion synthesize and generation. But at the same time it can also be applied to action recognition. With this idea, this paper extracts a kind of dynamic features from the original trajectory within DMP framework. The feature is temporal-spatial invariant. Based on the feature, FastDTW-KNN algorithm is proposed to solve the recognition task. Experiments tested on HAR dataset and handwritten letters dataset achieved an excellent recognition performance under a large data noise, which has verified the effectiveness of our method. In addition, comparative recognition experiments based on the original feature and our extracted dynamic feature are conducted. Results show that the dynamic feature is robust under temporal and spatial noise. As for classifiers, we compared our method with KNN, SVM and DTW-KNN followed with a detailed analysis of their advantages and disadvantages.

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Acknowledgements

This work is supported by National Natural Science Foundation of China (Grant Nos. 51505470).

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Authors and Affiliations

  1. State Key Laboratory of Robotics, Shenyang Institute of Automation, Shenyang, 110016, China

    Huiwen Zhang, Mingliang Fu, Haitao Luo & Weijia Zhou

  2. University of Chinese Academy of Science, Beijing, China

    Huiwen Zhang & Mingliang Fu

Authors
  1. Huiwen Zhang
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  2. Mingliang Fu
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  3. Haitao Luo
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  4. Weijia Zhou
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Corresponding author

Correspondence to Huiwen Zhang .

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Editors and Affiliations

  1. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

    YongAn Huang

  2. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

    Hao Wu

  3. Institute of Industrial Research, University of Portsmouth, Portsmouth, United Kingdom

    Honghai Liu

  4. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

    Zhouping Yin

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Zhang, H., Fu, M., Luo, H., Zhou, W. (2017). Robust Human Action Recognition Using Dynamic Movement Features. In: Huang, Y., Wu, H., Liu, H., Yin, Z. (eds) Intelligent Robotics and Applications. ICIRA 2017. Lecture Notes in Computer Science(), vol 10462. Springer, Cham. https://doi.org/10.1007/978-3-319-65289-4_45

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  • DOI: https://doi.org/10.1007/978-3-319-65289-4_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65288-7

  • Online ISBN: 978-3-319-65289-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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