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
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
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
