Publication Date: 2020/09/03
Abstract: In order to strengthen the monitoring of the elderly and reduce the safety risks caused by falls, a video-based indoor fall detection algorithm using a dual network structure is proposed. Firstly, for the recorded video stream, we apply the fine-tuned YOLACT network to extract the contours of the human body, and then design a simple convolutional neural network to distinguish the categories of different family activities (including bending, standing, sitting and lying), and finally make a fall decision. When a lying position is detected on the floor region, it is considered as a fall. Experiments show that the proposed algorithm can successfully detect fall events in different indoor scenarios, and have a low false detection rate on the constructed data set.
Keywords: health care; YOLACT; convolutional neural network; gesture recognition; fall detection
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT20AUG551_(1).pdf
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