Human fall detection using spatial temporal graph convolution

Sai Zhang; Chongyang Zhang

doi:10.1117/12.2679291

7 June 2023 Human fall detection using spatial temporal graph convolution

Sai Zhang, Chongyang Zhang

Proceedings Volume 12701, Fifteenth International Conference on Machine Vision (ICMV 2022); 1270105 (2023) https://doi.org/10.1117/12.2679291
Event: Fifteenth International Conference on Machine Vision (ICMV 2022), 2022, Rome, Italy

Abstract

Falls are a growing social problem and have become a hot topic in healthcare. Thanks to recent advances in deep convolutional neural networks, the accuracy of video-based fall detection has been greatly improved. However, these methods are affected by illumination and complex backgrounds. Video angles and other influencing factors reduce the accuracy and generalization ability of these methods. In this paper, a video-based human fall detection method is proposed. First, a 2D joint point sequence in the video is extracted using a pose estimator, and then a 2D joint point pose sequence is extracted. It is elevated to a 3D joint point pose sequence and then recognized whether it is a fall action by our improved multi-scale unified spatial-temporal graph convolutional network (MS-G3D). The system proves its effectiveness and robustness in the field of action recognition, achieving 99.84% accuracy on the large benchmark action recognition dataset NTU RGB+D, and 95.72% accuracy on the LE2I fall dataset.

Citation Download Citation

Sai Zhang and Chongyang Zhang "Human fall detection using spatial temporal graph convolution", Proc. SPIE 12701, Fifteenth International Conference on Machine Vision (ICMV 2022), 1270105 (7 June 2023); https://doi.org/10.1117/12.2679291

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KEYWORDS

Convolution

Video

Data modeling

Pose estimation

Education and training

RGB color model

Action recognition

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