广东工业大学学报 ›› 2023, Vol. 40 ›› Issue (04): 53-59.doi: 10.12052/gdutxb.220078
黄晓湧, 李伟彤
Huang Xiao-yong, Li Wei-tong
摘要: 跌倒行为会给老人特别独居老人带来严重伤害,准确识别跌倒并及时报警可以有效降低这种危险。本文提出一种基于树结构骨架图像(Tree Structure Skeleton Image,TSSI) 和可学习时空块卷积神经网络 (Spatio-temporal Block Convolution Neural Network,STB-CNN) 的跌倒检测方法。首先使用三维姿态估计算法提取人体关节点,进而获得骨架序列;然后利用基于深度优先搜索(Depth First Search,DFS) 算法将骨架序列编码为TSSI;最后构建由时空差分模块、可学习时空框架和时空多分支卷积模块组成的可学习STB-CNN网络,实现跌倒检测。该方法在公开数据集和自建数据集上进行仿真实验分别取得98.6%和98.3%的准确率,优于其他相关算法。
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