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Drowning behavior detection in swimming pool based on deep learning

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Abstract

In order to quickly help lifesavers judge whether people are drowning in the swimming pool, this paper proposes one efficient behavior recognition approach by means of video sequences of underwater. First, by analyzing the spatial distribution of swimming pool when swimmers are normally swimming, the data labeling and swimmer detection methods are determined. Second, a behavior recognition framework of swimmers on the basis of YOLOv4 algorithm (BR-YOLOv4) is proposed in this paper. The spatial relationship between the location information of the target and swimming/drowning area of swimming pool is analyzed to further determine the swimmer’s drowning or swimming behavior. This paper compares the detection accuracy of different detection algorithms and analyzes the detection effect of different pool angles and different swimmer densities. Test results show that the mean precision rate of drowning is 94.62%, the mean false rate is 1.43% , and the mean missing rate is 3.57%. The mean precision rate of swimming is 97.86%, the mean false rate is 7.93%, the mean missing rate is 5.93% , and the average frame rate is 33f/s. All the results show that the method proposed in this paper meets the real-time detection requirements and does well in swimmer behavior recognition and provides technical support for reducing drowning accidents in public swimming pools.

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, China

    Fei Lei, Hengyu Zhu, Feifei Tang & Xinyuan Wang

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  1. Fei Lei
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  2. Hengyu Zhu
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  3. Feifei Tang
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  4. Xinyuan Wang
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Correspondence to Hengyu Zhu.

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Lei, F., Zhu, H., Tang, F. et al. Drowning behavior detection in swimming pool based on deep learning. SIViP 16, 1683–1690 (2022). https://doi.org/10.1007/s11760-021-02124-9

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  • DOI: https://doi.org/10.1007/s11760-021-02124-9

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