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Action Recognition for Solo-Militant Based on ResNet and Rule Matching

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Data Mining and Big Data (DMBD 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1744))

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Abstract

To solve the problem of low accuracy of solo-militant action recognition under small sample data set, a new method of solo-militant behavior analysis based on ResNet and rule matching is proposed in this paper. The militant's action classification is done by 2 levels of classification. Firstly, the skeleton key points are extracted from the militant's combat video frames by OpenPose. Then, the first level classification of militant's action is performed by the ResNet deep learning network based on RGB images and combined with the skeleton key point rule set of militant's action. Next, the second level classification of militant's action is performed by the CNN network based on skeleton map. At last, the final classification of militant's action is output according to the 2 levels of classification. The experimental results show that the proposed method in this paper can achieve more effective recognition rate of solo-militant action under small sample data set.

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Author information

Authors and Affiliations

  1. School of Information Science and Technology, North China University of Technology, Beingjing, 100144, China

    Lijing Tong & Jinzhi Feng

  2. Advanced Institute of Big Data, Beijing, 100195, China

    Huiqun Zhao & Kun Liu

Authors
  1. Lijing Tong
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  2. Jinzhi Feng
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  3. Huiqun Zhao
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  4. Kun Liu
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Corresponding author

Correspondence to Kun Liu .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

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Tong, L., Feng, J., Zhao, H., Liu, K. (2022). Action Recognition for Solo-Militant Based on ResNet and Rule Matching. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2022. Communications in Computer and Information Science, vol 1744. Springer, Singapore. https://doi.org/10.1007/978-981-19-9297-1_15

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  • DOI: https://doi.org/10.1007/978-981-19-9297-1_15

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-9296-4

  • Online ISBN: 978-981-19-9297-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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