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

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.