红外技术2023，Vol.45Issue(12)：1314-1321,8.
基于改进Alphapose的红外图像人体摔倒检测算法
Infrared Image Human Fall Detection Algorithm Based on Improved Alphapose
张鹏 1沈玉真 1李培华 1张恺翔1
作者信息
- 1. 中航华东光电有限公司,安徽 芜湖 241002
- 折叠
摘要
Abstract
Human fall detection in infrared images is not affected by ambient light and has important research and application value in intelligent security.Existing fall detection methods do not fully consider the position change law of key points on the human body,which can easily cause false detections of similar fall movements.To solve this problem,we propose an infrared image fall detection algorithm based on an improved alpha pose.The algorithm uses the YOLO v5s object detection network to directly classify human poses while extracting the human body target frame and inputting the pose estimation network.It then evaluates it in combination with the position information and posture characteristics of the key points of the human skeleton.Experiments showed that the algorithm exhibited good performance in terms of accuracy and real-time performance.关键词
红外图像/摔倒检测/关键点/目标检测Key words
infrared images/fall detection/key points/object detection分类
计算机与自动化引用本文复制引用
张鹏,沈玉真,李培华,张恺翔..基于改进Alphapose的红外图像人体摔倒检测算法[J].红外技术,2023,45(12):1314-1321,8.
