The current computer vision-based elderly falling behavior recognition algorithm mainly uses target detection followed by behavior recognition for a single human body, but there are mostly numerous human targets and n...
详细信息
The current computer vision-based elderly falling behavior recognition algorithm mainly uses target detection followed by behavior recognition for a single human body, but there are mostly numerous human targets and noise interference in the actual scene, which makes it difficult to accurately detect and recognize the anomalous behavior of the moving individual, for which detection and tracking of the moving human body is needed. In this paper, the fusion applications of histogram of oriented gradients human body detection algorithm, Camshift human body tracking algorithm, and spatial temporal graph convolutional networks gesture recognition algorithm are investigated for the recognition of anomalous behaviors in elderly people during falls. After testing with 100 motion target groups, the time lag of this paper's fusion algorithm for motion human falling behavior recognition is < 0.5 s on average, and the average accuracy of anomalous falling behavior recognition is 97.0%, which is better than that of falling behavior recognition based on acquisition devices such as environmental sensors and deep learning models such as YOLOv5 and thus provides more powerful protection for the personal safety of elderly people.
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