天津工业大学学报2026，Vol.45Issue(4)：80-88,95,10.DOI:10.3969/j.issn.1671-024x.2026.04.010
基于二维和三维人体姿态估计的跌倒检测
Fall detection based on 2D and 3D human pose estimation
摘要
Abstract
To address the problem of false positives and false negatives in the existing fall detection technologies,a fall detection strategy integrating two-dimensional and three-dimensional human pose estimation is proposed.Based on the Mamba model,the Mamba-Attention mechanism is designed,the C3_M module is enhanced,and the 2D human pose estimation algorithm MambaPose is introduced.By integrating Flash-Attention,DCT transform and low-pass filtering,the 3D human pose estimation algorithm FlashPose is proposed to improve the accuracy and real-time performance of mapping 2D human keypoints to 3D space.Finally,MFKF,a high accuracy real-time fall detection method,is constructed by combining MambaPose,FlashPose,and the KNN classifier.The experimental results on the public datasets show that:On the COCO dataset,the MambaPose algorithm improves the AP value by 2.5 percentage points compared with YOLOv7Pose,which significantly improves the accuracy of 2D human keypoint detection,and has higher confidence in small target detection and greater robustness when processing low-resolution images;on the Human3.6M dataset,the FlashPose al-gorithm's MPJPE and FPS values reach 47.2 mm and 314,respectively,which are better than Pose-FormerV2,with higher accuracy and faster computation speed;on the three fall detection datasets of UR,Le2i,and Multicam,the proposed MFKF method has the optimal F1,sensitivity,specificity,and accuracy indexes,and the FPS values are higher than 22.7,which realizes accurate and real-time detection of falls.Code and demonstration videos are available at https://github.com/xgli411/MFKF.关键词
跌倒检测/二维人体姿态估计/三维人体姿态估计/K近邻分类器(KNN)/Mamba模型Key words
fall detection/2D human pose estimation/3D human pose estimation/KNN classifier/Mamba model分类
信息技术与安全科学引用本文复制引用
李现国,段显霖,杨彦利,蒋鹏,张磊..基于二维和三维人体姿态估计的跌倒检测[J].天津工业大学学报,2026,45(4):80-88,95,10.基金项目
天津市技术创新引导专项(23YDTPJC00290) （23YDTPJC00290）
