摘要
基于自然场景图像的人体行为识别方法中遮挡、背景干扰、光照不均匀等因素影响识别结果,利用人体三维骨架序列的行为识别方法可以克服上述缺点。首先,考虑人体行为的时空特性,提出一种时空特征融合深度学习网络人体骨架行为识别方法;其次,根据骨架几何特征建立视角不变性特征表示,CNN(Convolutional Neural Network)网络学习骨架的局部空域特征,作用于空域的LSTM(Long Short Term Memory)网络学习骨架空域节点之间的相关性特征,作用于时域的LSTM网络学习骨架序列时空关联性特征;最后,利用NTU RGB+D数据库验证文中算法。实验结果表明:算法识别精度有所提高,对于多视角骨架具有较强的鲁棒性。
Action recognition from natural scene was affected by complex illumination conditions and cluttered backgrounds.There was a growing interest in solving these problems by using 3D skeleton data.Firstly,considering the spatio-temporal features of human actions,a spatio-temporal fusion deep learning network for action recognition was proposed;Secondly,view angle invariant character was constructed based on geometric features of the skeletons.Local spatial character was extracted by short-time CNN networks.A spatio-LSTM network was used to learn the relation between joints of a skeleton frame.Temporal LSTM was used to learn spatio-temporal relation between skeleton sequences.Lastly,NTU RGB+D datasets were used to evaluate this network,the network proposed achieved the state-of-the-art performance for 3D human action analysis.Experimental results show that this network has strong robustness for view invariant sequences.
作者
裴晓敏
范慧杰
唐延东
Pei Xiaomin;Fan Huijie;Tang Yandong(School of Information and Control Engineering,Liaoning Shihua University,Fushun 113001,China;State Key Laboratory of Robotics,Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016,China)
出处
《红外与激光工程》
EI
CSCD
北大核心
2018年第2期46-51,共6页
Infrared and Laser Engineering
基金
国家自然科学基金(61401455)
关键词
时空特征
融合
骨架
视角不变
spatio-temporal feature
fusion
skeleton
view invariant
作者简介
裴晓敏(1981-)，女，讲师，博士后，主要从事机器视觉方面的研究。Email:pxm_neu@126.com;唐延东(1962-)，男，博士生导师，博士，主要从事图像处理与模式识别、机器视觉方面的研究。Email:ytang@sia.cn
