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http://dx.doi.org/10.33851/JMIS.2020.7.4.257

Fall Situation Recognition by Body Centerline Detection using Deep Learning  

Kim, Dong-hyeon (Dept. of Computer Software Engineering, Dong-eui University)
Lee, Dong-seok (Dept. of Computer Software Engineering, Dong-eui University)
Kwon, Soon-kak (Dept. of Computer Software Engineering, Dong-eui University)
Publication Information
Journal of Multimedia Information System / v.7, no.4, 2020 , pp. 257-262 More about this Journal
Abstract
In this paper, a method of detecting the emergency situations such as body fall is proposed by using color images. We detect body areas and key parts of a body through a pre-learned Mask R-CNN in the images captured by a camera. Then we find the centerline of the body through the joint points of both shoulders and feet. Also, we calculate an angle to the center line and then calculate the amount of change in the angle per hour. If the angle change is more than a certain value, then it is decided as a suspected fall. Also, if the suspected fall state persists for more than a certain frame, then it is determined as a fall situation. Simulation results show that the proposed method can detect body fall situation accurately.
Keywords
Mask-RCNN; Body Fall; Color Video; Action Recognition;
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Times Cited By KSCI : 2  (Citation Analysis)
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