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Occlusion-Aware Skeleton Trajectory Representation for Abnormal Behavior Detection

  • Onur TemurogluEmail author
  • Yasutomo Kawanishi
  • Daisuke Deguchi
  • Takatsugu Hirayama
  • Ichiro Ide
  • Hiroshi Murase
  • Mayuu Iwasaki
  • Atsushi Tsukada
Conference paper
  • 137 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1212)

Abstract

Surveillance cameras are expected to play a large role in the development of ITS technologies. They can be used to detect abnormally behaving individuals which can then be reported to drivers nearby. There are multiple works that tackle the problem of abnormal behavior detection. However, most of these works make use of appearance features which have redundant information and are susceptible to noise. While there are also works that make use of pose skeleton representation, they do not consider well how to handle cases with occlusions, which can occur due to the simple reason of pedestrian orientation preventing some joints from appearing in the frame clearly. In this paper, we propose a skeleton trajectory representation that enables handling of occlusions. We also propose a framework for pedestrian abnormal behavior detection that uses the proposed representation and detect relatively hard-to-notice anomalies such as drunk walking. The experiments we conducted show that our method outperforms other representation methods.

Keywords

Pose skeleton Anomaly detection Surveillance cameras 

Notes

Acknowledgment

Parts of this research were supported by MEXT, Grants-in-Aid for Scientific Research.

References

  1. 1.
    Bera, A., Kim, S., Manocha, D.: Realtime anomaly detection using trajectory-level crowd behavior learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 50–57 (2016)Google Scholar
  2. 2.
    Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: Realtime multi-person 2D pose estimation using part affinity fields. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7291–7299 (2017)Google Scholar
  3. 3.
    Fang, H.S., Xie, S., Tai, Y.W., Lu, C.: RMPE: regional multi-person pose estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2334–2343 (2017)Google Scholar
  4. 4.
    Hasan, M., Choi, J., Neumann, J., Roy-Chowdhury, A.K., Davis, L.S.: Learning temporal regularity in video sequences. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 733–742 (2016)Google Scholar
  5. 5.
    Hinami, R., Mei, T., Satoh, S.: Joint detection and recounting of abnormal events by learning deep generic knowledge. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3619–3627 (2017)Google Scholar
  6. 6.
    Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Morais, R., Le, V., Tran, T., Saha, B., Mansour, M., Venkatesh, S.: 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 (2019)Google Scholar
  8. 8.
    Papandreou, G., Zhu, T., Chen, L.-C., Gidaris, S., Tompson, J., Murphy, K.: PersonLab: person pose estimation and instance segmentation with a bottom-up, part-based, geometric embedding model. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. LNCS, vol. 11218, pp. 282–299. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01264-9_17CrossRefGoogle Scholar
  9. 9.
    Piciarelli, C., Micheloni, C., Foresti, G.L.: Trajectory-based anomalous event detection. IEEE Trans. Circuits Syst. Video Technol. 18(11), 1544–1554 (2008)CrossRefGoogle Scholar
  10. 10.
    Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)Google Scholar
  11. 11.
    Sakurada, M. Yairi, T.: Anomaly detection using autoencoders with nonlinear dimensionality reduction. In: Proceedings of the MLSDA 2nd Workshop on Machine Learning for Sensory Data Analysis, 4 p. (2014)Google Scholar
  12. 12.
    Sekii, T.: Pose proposal networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 350–366. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01261-8_21CrossRefGoogle Scholar
  13. 13.
    Sultani, W., Chen, C., Shah, M.: Real-world anomaly detection in surveillance videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6479–6488 (2018)Google Scholar
  14. 14.
    Zimek, A., Schubert, E., Kriegel, H.P.: A survey on unsupervised outlier detection in high-dimensional numerical data. Stat. Anal. Data Min. ASA Data Sci. J. 5(5), 363–387 (2012)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Onur Temuroglu
    • 1
    Email author
  • Yasutomo Kawanishi
    • 1
  • Daisuke Deguchi
    • 1
  • Takatsugu Hirayama
    • 1
  • Ichiro Ide
    • 1
  • Hiroshi Murase
    • 1
  • Mayuu Iwasaki
    • 2
  • Atsushi Tsukada
    • 2
  1. 1.Nagoya UniversityNagoyaJapan
  2. 2.Sumitomo Electric Industries, Ltd.OsakaJapan

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