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International Conference on Intelligent Robotics and Applications

ICIRA 2022: Intelligent Robotics and Applications pp 450–458Cite as

Video Abnormal Behavior Detection Based on Human Skeletal Information and GRU

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13456))

Abstract

Video abnormal behavior detection is an important research direction in the field of computer vision and has been widely used in surveillance video. This paper proposes an abnormal behavior detection model based on human skeletal structure and recurrent neural network, which uses dynamic skeletal features for abnormal behavior detection. The model in this paper extracts key points from multiple frames through the human pose estimation algorithm, obtains human skeleton information, and inputs it into the established autoencoder recurrent neural network to detect abnormal human behavior from video sequences. Compared with traditional appearance-based models, our method has better anomaly detection performance under multi-scene and multi-view.

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References

  1. Chalapathy, R., Menon, A.K., Chawla, S.: Robust, deep and inductive anomaly detection. In: Ceci, M., Hollmén, J., Todorovski, L., Vens, C., Džeroski, S. (eds.) ECML PKDD 2017. LNCS (LNAI), vol. 10534, pp. 36–51. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71249-9_3

    Chapter  Google Scholar 

  2. Ravanbakhsh, M., Nabi, M., Sangineto, E., et al.: Abnormal event detection in videos using generative adversarial nets. In: Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), pp. 1577–1581. IEEE, Beijing (2017)

    Google Scholar 

  3. Li, W., Mahadevan, V., Vasconcelos, N.: Anomaly detection and localization in crowded scenes. IEEE Trans. Pattern Anal. Mach. Intell. 36(1), 18–32 (2013)

    Google Scholar 

  4. Tran, D., Bourdev, L., Fergus, R., et al.: Learning spatiotemporal features with 3D convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4489–4497. IEEE, Santiago (2015)

    Google Scholar 

  5. Feichtenhofer, C., Pinz, A., Zisserman, A.: Convolutional two-stream network fusion for videoaction recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1933–1941. IEEE, Las Vegas (2016)

    Google Scholar 

  6. Yue-Hei Ng, J., Hausknecht, M., Vijayanarasimhan, S., et al.: Beyond short snippets: Deep networks for video classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4694–4702. IEEE, Boston (2015)

    Google Scholar 

  7. Tian, X.M., Fan, J.Y.: Joints kinetic and relational features for action recognition. Signal Process.: The Official Publication of the European Association for Signal Processing (EURASIP) 142, 412–422 (2018)

    Google Scholar 

  8. Morais, R., Le, V., Tran, T., et al.: Learning regularity in skeleton trajectories for anomaly detection in videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 11996–12004. IEEE, Long Beach (2019)

    Google Scholar 

  9. Cho, K., van Merrienboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: Encoder-decoder approaches. CoRR, 2014, abs/1409. 1259

    Google Scholar 

  10. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  11. Cao, Z., Simon, T., Wei, S.E., et al.: Realtime multi-person 2D pose estimation using part affinity fields. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1302–1310. IEEE, Honolulu (2017)

    Google Scholar 

  12. Chong, Y.S., Tay, Y.H.: Abnormal event detection in videos using spatiotemporal autoencoder. In: Cong, F., Leung, A., Wei, Q. (eds.) ISNN 2017. LNCS, vol. 10262, pp. 189–196. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59081-3_23

    Chapter  Google Scholar 

  13. Luo, W., Liu, W., Gao, S.: A revisit of sparse coding based anomaly detection in stacked rnn framework. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 341–349. IEEE, Venice (2017)

    Google Scholar 

  14. Hasan, M., Choi, J., Neumann, J., et al.: Learning temporal regularity in video sequences. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 733–742. IEEE, Las Vegas (2016)

    Google Scholar 

  15. Liu, W., Luo, W., Lian, D., et al.: Future frame prediction for anomaly detection-a new baseline. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6536–6545. IEEE, Salt Lake City (2018)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Shenyang Aerospace University, Shenyang, Liaoning, China

    Yibo Li & Zixun Zhang

Authors
  1. Yibo Li
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  2. Zixun Zhang
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Corresponding author

Correspondence to Yibo Li .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, China

    Honghai Liu

  2. Huazhong University of Science and Technology, Wuhan, China

    Zhouping Yin

  3. Shenyang Institute of Automation, Shenyang, Liaoning, China

    Lianqing Liu

  4. Harbin Institute of Technology, Harbin, China

    Li Jiang

  5. Shanghai Jiao Tong University, Shanghai, China

    Guoying Gu

  6. Shenzhen Institutes of Advanced Technology, Shenzhen, China

    Xinyu Wu

  7. Harbin Institute of Technology, Shenzhen, China

    Weihong Ren

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© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Cite this paper

Li, Y., Zhang, Z. (2022). Video Abnormal Behavior Detection Based on Human Skeletal Information and GRU. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13456. Springer, Cham. https://doi.org/10.1007/978-3-031-13822-5_40

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  • DOI: https://doi.org/10.1007/978-3-031-13822-5_40

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13821-8

  • Online ISBN: 978-3-031-13822-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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