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Sai Zhang

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Proceedings Article | 7 June 2023 Paper

Human fall detection using spatial temporal graph convolution

Sai Zhang, Chongyang Zhang

Proceedings Volume 12701, 1270105 (2023) https://doi.org/10.1117/12.2679291

KEYWORDS: Convolution, Video, Data modeling, Pose estimation, Education and training, RGB color model, Matrices, Feature extraction, Detection and tracking algorithms, Action recognition

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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.

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