Real Time Gesture (Fall) Recognition of Traffic Video Based on Multi-resolution Human Skeleton Analysis

  • Xinchen Xu
  • Xiaoqing Zeng
  • Yizeng WangEmail author
  • Qipeng Xiong
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 127)


The objective of this study was to detect abnormal behavior event (fall) by analyzing the existing monitoring video for ensuring the safety of rail transit platform passengers. Here, the key point coordinates and limb joints of human body are obtained based on PAFs (part affinity fields). Then feature information determined the fall is extracted based on the neck key point tracking algorithm. The feature for judging fall, namely the angle between leg and horizontal plane, is proposed in this study. The experimental data is based on the simulated fall videos took from Leqiao station of Soochow 1st metro line in Jiangsu province. Our results show that, the single picture and video are tested respectively, and it is found that the timing information is more fault-tolerant and more accurate in identifying falls, yet is more complex and more difficult to implement. What’s more, because of the lack of sufficient fall videos, the results analysis based on the proposed algorithm remains much room for improvements. Also, more useful and efficient detection characteristics can be taken into account in the future.


Gesture recognition Rail transit Video surveillance Fall detection 


  1. 1.
    Tompson, J.J., Jain, A., LeCun, Y., et al.: Joint training of a convolutional network and a graphical model for human pose estimation. In: Advances in Neural Information Processing Systems, pp. 1799–1807 (2014)Google Scholar
  2. 2.
    Aslan, M., Sengur, A., Xiao, Y., et al.: Shape feature encoding via fisher vector for efficient fall detection in depth-videos. Appl. Soft Comput. 37, 1023–1028 (2015)CrossRefGoogle Scholar
  3. 3.
    Ke, S.R., Thuc, H.L.U., Lee, Y.J., et al.: A review on video-based human activity recognition. Computers 2(2), 88–131 (2013)CrossRefGoogle Scholar
  4. 4.
    Cao, Z., Simon, T., Wei, S.E., et al.: Realtime multi-person 2D pose estimation using part affinity fields. In: CVPR 2017, vol. 1, no. 2, p. 7 (2017)Google Scholar
  5. 5.
    Foroughi, H., Pourreza, H.R.: Intelligent video surveillance for monitoring fall detection of elderly in home environments. In: 11th International Conference on Computer and Information Technology (2008)Google Scholar
  6. 6.
    Rougier, C., Meunier, J., St-Arnaud, A., et al.: Fall detection from human shape and motion history using video surveillance. In: 2007 21st International Conference on Advanced Information Networking and Applications Workshops, AINAW 2007, vol. 2, pp. 875–880. IEEE (2007)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Xinchen Xu
    • 1
  • Xiaoqing Zeng
    • 1
  • Yizeng Wang
    • 2
    Email author
  • Qipeng Xiong
    • 3
  1. 1.The Key Laboratory of Road and Traffic Engineering, Ministry of EducationTongji UniversityShanghaiChina
  2. 2.Shanghai UniversityShanghaiChina
  3. 3.Shanghai FR Traffic Technology Limited CorporationShanghaiChina

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