动作识别
计算机科学
动作(物理)
人工智能
鉴定(生物学)
活动识别
期限(时间)
运动(物理)
短时记忆
机器学习
人体运动
语音识别
感知
人机交互
模式识别(心理学)
计算机视觉
人工神经网络
心理学
循环神经网络
物理
植物
量子力学
神经科学
生物
班级(哲学)
作者
H. J. Lee,Shu-Lin Hsieh,Ming-Fong Tsai
标识
DOI:10.1109/icce-taiwan58799.2023.10226656
摘要
Human motion recognition technology has started to be applied in many important research fields, for example to assist inpatient rehabilitation and for the perception of dangerous activity. Hence, improving the accuracy of human motion recognition is a very important research topic. At present, human action recognition systems have only been developed for actions by a single person. In this paper, we propose to use LSTM technology for multi-person human action recognition model training and identification. We compare the performance of our approach with that of the ST-GCN human action recognition framework, and find that it improves the recognition accuracy by 49.69%.
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