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Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks
Wang Hongsong(王洪松)1,2,3,4; Wang Liang(王亮)1,2,3,4; Liang Wang
2017
会议名称IEEE Computer Vision and Pattern Recognition (CVPR)
会议日期July 22 - July 25 2017
会议地点Honolulu, Hawai
摘要Recently, skeleton based action recognition gains more popularity due to cost-effective depth sensors coupled with real-time skeleton estimation algorithms. Traditional approaches based on handcrafted features are limited to represent the complexity of motion patterns. Recent methods that use Recurrent Neural Networks (RNN) to handle raw skeletons only focus on the contextual dependency in the temporal domain and neglect the spatial configurations of articulated skeletons. In this paper, we propose a novel two-stream RNN architecture to model both temporal dynamics and spatial configurations for skeleton based action recognition. We explore two different structures for the temporal stream: stacked RNN and hierarchical RNN. Hierarchical RNN is designed according to human body kinematics. We also propose two effective methods to model the spatial structure by converting the spatial graph into a sequence of joints. To improve generalization of our model, we further exploit 3D transformation based data augmentation techniques including rotation and scaling transformation to transform the 3D coordinates of skeletons during training. Experiments on 3D action recognition benchmark datasets show that our method brings a considerable improvement for a variety of actions, i.e., generic actions, interaction activities and gestures.
关键词Action Recognition Temporal Dynamics Spatial Configurations
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/19624
专题智能感知与计算研究中心
通讯作者Liang Wang
作者单位1.Center for Research on Intelligent Perception and Computing (CRIPAC)
2.National Laboratory of Pattern Recognition (NLPR)
3.Institute of Automation, Chinese Academy of Sciences (CASIA)
4.University of Chinese Academy of Sciences (UCAS)
推荐引用方式
GB/T 7714
Wang Hongsong,Wang Liang,Liang Wang. Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks[C],2017.
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