Fall detection based on OpenPose and MobileNetV2 network. Issue 3 (22nd October 2022)

• Record Type: Journal Article
• Authors: Gao, Mengqi; Li, Jiangjiao; Zhou, Dazheng; Zhi, Yumin; Zhang, Mingliang; Li, Bin
• Is Part Of: IET image processing. Volume 17:Issue 3(2023)
• Page Start: 722
• Page End: 732
• Publication Date: 2022-10-22
• Subjects: Image processing -- Periodicals; 621.36705
• DOI: 10.1049/ipr2.12667
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract: The proposed fall detection approach is aimed at building a support system for the elders. In this work, a method based on human pose estimation and lightweight neural network is used to detect falls. First, the OpenPose is used to extract human keypoints and label them in the images. After that, the modified MobileNetV2 network is used to detect falls by integrating both human keypoint information and pose information in the original images. The above operation can use the original image information to correct the deviation in the keypoint labeling process. Through experiments, the accuracy of the proposed method is 98.6% and 99.75% on the UR and Le2i datasets, which is higher than the listed comparison methods.