红外技术, 2023, 45 (12): 1314, 网络出版: 2024-01-17  

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
作者单位
中航华东光电有限公司, 安徽芜湖 241002
摘要
红外图像中的人体摔倒检测不受环境光照射的影响, 在智能安防领域有着重要的研究意义和应用价值。现有的摔倒检测方法没有充分考虑人体关键点的位置变化规律, 容易对类摔倒动作造成误检。针对这一问题, 本文提出一种基于改进 Alphapose的红外图像摔倒检测算法。该算法使用 Yolo v5s目标检测网络, 在提取人体目标框输入姿态估计网络的同时, 对人体姿态进行直接分类, 再结合人体骨架关键点的位置信息和姿态特征进行判断。通过实验证明, 该算法在准确度和实时性方面都有良好的表现。
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.
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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. ZHANG Peng, SHEN Yuzhen, LI Peihua, ZHANG Kaixiang. Infrared Image Human Fall Detection Algorithm Based on Improved Alphapose[J]. Infrared Technology, 2023, 45(12): 1314.

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