Fall detection based on lightweight OpenPose algorithm

Abstract

Falls is one of sudden, unintentional changes in body position, which usually occur in the elderly and may seriously affect the physical and mental health of the elderly. Thanks to the rapid development of computer vision algorithms, fall detection research based on RGB images or videos has gradually become the mainstream framework for fall detection due to its rich semantics, low cost, and friendly user experience. In this paper, as a means of improving the accuracy of fall detection in real time, a lightweight fall detection method based on Lightweight OpenPose is proposed. Specifically, the proposed method first calculates the skeleton map and joint point coordinates of the human body in real time based on the Lightweight OpenPose. Then, the obtained coordinates are combined with the proposed fall detection algorithm for detection. Through extensive experiments, we qualitatively and quantitatively verify the effectiveness of our method. At the end of this paper, we also put forward some views on the further improvement measures of this algorithm.