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Hierarchical recurrent neural network for skeleton based action recognition
Du, Yong1,2; Wang, Wei1,2; Wang, Liang1,2
2015-06
会议名称2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
会议录名称2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
会议日期2015-6
会议地点Boston, MA, USA
摘要Human actions can be represented by the trajectories of skeleton joints. Traditional methods generally model the spatial structure and temporal dynamics of human skeleton with hand-crafted features and recognize human actions by well-designed classifiers. In this paper, considering that recurrent neural network (RNN) can model the long-term contextual information of temporal sequences well, we propose an end-to-end hierarchical RNN for skeleton based action recognition. Instead of taking the whole skeleton as the input, we divide the human skeleton into five parts according to human physical structure, and then separately feed them to five subnets. As the number of layers increases, the representations extracted by the subnets are hierarchically fused to be the inputs of higher layers. The final representations of the skeleton sequences are fed into a single-layer perceptron, and the temporally accumulated output of the perceptron is the final decision. We compare with five other deep RNN architectures derived from our model to verify
the effectiveness of the proposed network, and also compare with several other methods on three publicly available datasets. Experimental results demonstrate that our model achieves the state-of-the-art performance with high computational efficiency.
关键词Hierarchical Recurrent Neural Network Action Recognition Skeleton Lstm
收录类别EI
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/11093
专题智能感知与计算研究中心
通讯作者Wang, Liang
作者单位1.Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern Recognition
2.Nat’l Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
推荐引用方式
GB/T 7714
Du, Yong,Wang, Wei,Wang, Liang. Hierarchical recurrent neural network for skeleton based action recognition[C],2015.
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