裴晓敏, 范慧杰, 唐延东. 时空特征融合深度学习网络人体行为识别方法[J]. 红外与激光工程, 2018, 47(2): 203007-0203007(6). DOI: 10.3788/IRLA201847.0203007
引用本文: 裴晓敏, 范慧杰, 唐延东. 时空特征融合深度学习网络人体行为识别方法[J]. 红外与激光工程, 2018, 47(2): 203007-0203007(6). DOI: 10.3788/IRLA201847.0203007
Pei Xiaomin, Fan Huijie, Tang Yandong. Action recognition method of spatio-temporal feature fusion deep learning network[J]. Infrared and Laser Engineering, 2018, 47(2): 203007-0203007(6). DOI: 10.3788/IRLA201847.0203007
Citation: Pei Xiaomin, Fan Huijie, Tang Yandong. Action recognition method of spatio-temporal feature fusion deep learning network[J]. Infrared and Laser Engineering, 2018, 47(2): 203007-0203007(6). DOI: 10.3788/IRLA201847.0203007

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

  • 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。

     

    Abstract: 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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