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

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International Symposium for Intelligent Transportation and Smart City (ITASC) 2019 Proceedings (ITASC 2019)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 127))

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Abstract

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.

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Authors and Affiliations

  1. The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, No. 4800 Cao’an Road, Shanghai, 201804, China

    Xinchen Xu & Xiaoqing Zeng

  2. Shanghai University, Shanghai, China

    Yizeng Wang

  3. Shanghai FR Traffic Technology Limited Corporation, Shanghai, China

    Qipeng Xiong

Authors
  1. Xinchen Xu
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  2. Xiaoqing Zeng
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  3. Yizeng Wang
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  4. Qipeng Xiong
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Corresponding author

Correspondence to Yizeng Wang .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, Shanghai, China

    Xiaoqing Zeng

  2. Department of Geotechnical Engineering, Tongji University , Shanghai, Shanghai, China

    Xiongyao Xie

  3. Shanghai Jiao Tong University , Shanghai, Shanghai, China

    Jian Sun

  4. School of Environmental Science and Engineering, Tongji University , Shanghai, Shanghai, China

    Limin Ma

  5. School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA

    Yinong Chen

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© 2019 Springer Nature Singapore Pte Ltd.

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Xu, X., Zeng, X., Wang, Y., Xiong, Q. (2019). Real Time Gesture (Fall) Recognition of Traffic Video Based on Multi-resolution Human Skeleton Analysis. In: Zeng, X., Xie, X., Sun, J., Ma, L., Chen, Y. (eds) International Symposium for Intelligent Transportation and Smart City (ITASC) 2019 Proceedings. ITASC 2019. Smart Innovation, Systems and Technologies, vol 127. Springer, Singapore. https://doi.org/10.1007/978-981-13-7542-2_1

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  • DOI: https://doi.org/10.1007/978-981-13-7542-2_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-7541-5

  • Online ISBN: 978-981-13-7542-2

  • eBook Packages: EngineeringEngineering (R0)

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