Citation

He X, Yuan F, Liu T, Zhu Y. Neural Comput Appl 2023; ePub(ePub): ePub.

Copyright

(Copyright © 2023, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s00521-023-08526-9

PMID

unavailable

Abstract

Computer vision combined with deep learning technologies is widely used in video surveillance. In this paper, it is applied to drowning detection video systems. Traditional drowning detection methods detect drowning mainly by monitoring the physiological condition, the time and motion of swimmers in the water. But these methods are not applicable to detect early quiet drowning phenomena. Some researchers realize supervised classification by simulative drowning features. But real drowning events are difficult to truly simulate, so these methods are not reliable. In this paper, a drowning detection video system with edge computing is proposed, and it can detect drowning events in swimming pools without any wearable devices. According to the characteristics of drowning people, the strategies for underwater near-vertical human detection are proposed, providing a reliable basis for drowning detection. A lightweight drowning detection convolutional autoencoder is proposed to achieve unsupervised drowning detection, solving the lack of drowning videos and the inauthenticity of simulative videos. Then, an edge device is designed for detecting drowning in real time at the edge. Finally, for training and experimental evaluation, a pool dataset including many pool underwater video sequences is produced. The experimental results show that the proposed drowning detection method has a good comprehensive performance. The system is feasible and valuable.

Language: en

Keywords

Convolutional autoencoder; Drowning detection; Underwater videos; Unsupervised learning