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.