时空特征融合深度学习网络人体行为识别方法
Action recognition method of spatio-temporal feature fusion deep learning network
查看参考文献14篇
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文摘
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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。 |
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其他语种文摘
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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. |
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来源
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红外与激光工程
,2018,47(2):0203007-1-0203007-6 【核心库】
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DOI
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10.3788/IRLA201847.0203007
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关键词
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时空特征
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融合
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骨架
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视角不变
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地址
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1.
辽宁石油化工大学信息与控制工程学院, 辽宁, 抚顺, 113001
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中国科学院沈阳自动化研究所, 机器人学国家重点实验室, 辽宁, 沈阳, 110016
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1007-2276 |
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学科
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自动化技术、计算机技术 |
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基金
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国家自然科学基金
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文献收藏号
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CSCD:6207499
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14
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