Human fall detection scheme based on YOLO visual recognition and embedded ARM architecture

Zhuoya Jia; Hanbo Zhang; Yang Jia; Yunjing Zheng; Dong Li; Shaobo Jia

doi:10.1117/12.2678904

8 May 2023 Human fall detection scheme based on YOLO visual recognition and embedded ARM architecture

Zhuoya Jia, Hanbo Zhang, Yang Jia, Yunjing Zheng, Dong Li, Shaobo Jia

Proceedings Volume 12635, Second International Conference on Algorithms, Microchips, and Network Applications (AMNA 2023); 1263514 (2023) https://doi.org/10.1117/12.2678904
Event: International Conference on Algorithms, Microchips, and Network Applications 2023, 2023, Zhengzhou, China

Abstract

This paper proposes a fall detection technology based on the YOLOv5s algorithm to solve the problem of hit injury. The method is designed based on the embedded ARM development board of Orange Pi Zero 2. The camera is used to collect human data in real-time, and algorithms train the collected data and are finally verified. The experimental results show that: (1) this method has a reasonable success rate of recognition for standing, walking, and falling, but the success rate of recognition for squatting needs to be improved; (2) Compared with the OpenPose algorithm, the YOLOv5 algorithm has better accuracy, precision, and average accuracy means, but the performance in recall rate is not very good.

Citation Download Citation

Zhuoya Jia, Hanbo Zhang, Yang Jia, Yunjing Zheng, Dong Li, and Shaobo Jia "Human fall detection scheme based on YOLO visual recognition and embedded ARM architecture", Proc. SPIE 12635, Second International Conference on Algorithms, Microchips, and Network Applications (AMNA 2023), 1263514 (8 May 2023); https://doi.org/10.1117/12.2678904

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