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Video Based Fall Detection Using Human Poses

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Big Data (BigData 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1496))

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Abstract

Video based fall detection accuracy has been largely improved due to the recent progress on deep convolutional neural networks. However, there still exist some challenges, such as lighting variation, complex background, which degrade the accuracy and generalization ability of these approaches. Meanwhile, large computation cost limits the application of existing fall detection approaches. To alleviate these problems, a video based fall detection approach using human poses is proposed in this paper. First, a lightweight pose estimator extracts 2D poses from video sequences, and then 2D poses are lifted to 3D poses. Second, we introduce a robust fall detection network to recognize fall events using estimated 3D poses, which increases respective field and maintains low computation cost by dilated convolutions. The experimental results show that the proposed fall detection approach achieves a high accuracy of 99.83% on large benchmark action recognition dataset NTU RGB+D and real-time performance of 18 FPS on a non-GPU platform, 63 FPS on a GPU platform.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China under Nos. 61773117 and 62006041.

Author information

Authors and Affiliations

  1. School of Automation, Southeast University, Nanjing, 210096, China

    Ziwei Chen, Yiye Wang & Wankou Yang

  2. Key Lab of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing, 210096, China

    Ziwei Chen, Yiye Wang & Wankou Yang

Authors
  1. Ziwei Chen
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  2. Yiye Wang
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  3. Wankou Yang
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Corresponding author

Correspondence to Wankou Yang .

Editor information

Editors and Affiliations

  1. National University of Defense Technology, Changsha, China

    Xiangke Liao

  2. Shenzhen University of Technology, Chinese Academy of Sciences, Shenzhen, China

    Wei Zhao

  3. University of Science and Technology of China, Hefei, China

    Enhong Chen

  4. Sun Yat-sen University, Guangzhou, China

    Nong Xiao

  5. Taiyuan University of Technology, Taiyuan, China

    Li Wang

  6. Nanjing University, Nanjing, China

    Yang Gao

  7. Nanjing University, Nanjing, China

    Yinghuan Shi

  8. Sun Yat-sen University, Guangzhou, China

    Changdong Wang

  9. Sun Yat-sen University, Guangzhou, China

    Dan Huang

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Chen, Z., Wang, Y., Yang, W. (2022). Video Based Fall Detection Using Human Poses. In: Liao, X., et al. Big Data. BigData 2022. Communications in Computer and Information Science, vol 1496. Springer, Singapore. https://doi.org/10.1007/978-981-16-9709-8_19

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  • DOI: https://doi.org/10.1007/978-981-16-9709-8_19

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-9708-1

  • Online ISBN: 978-981-16-9709-8

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