A Computer Vision-Based Dual Network Approach for Indoor Fall Detection

Authors : Lu Zhang; Chun Fang; Ming Zhu

Volume/Issue : Volume 5 - 2020, Issue 8 - August

Google Scholar : http://bitly.ws/9nMw

Scribd : https://bit.ly/3hSz16o

DOI : 10.38124/IJISRT20AUG551

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


Paper Submission Last Date
31 - July - 2022

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