CASIA OpenIR  > 智能感知与计算研究中心
Skeleton Based Action Recognition with Convolutional Neural Network
Du, Yong1,3; Fu, Yun4; Wang, Liang1,2,3
2015
Conference NameAsian Conference on Pattern Recognition
Source PublicationAsian Conference on Pattern Recognition
Conference Date2015-11
Conference PlaceKuala Lumpur, Malaysia
AbstractTemporal dynamics of postures over time is crucial for sequence-based action recognition. Human actions can be represented by the corresponding motions of articulated skeleton. Most of the existing approaches for skeleton based action recognition model the spatial-temporal evolution of actions based on hand-crafted features. As a kind of hierarchically adaptive filter banks, Convolutional Neural Network (CNN) performs well in representation learning. In this paper, we propose an end-to-end hierarchical architecture for skeleton based action recognition with CNN. Firstly, we represent a skeleton sequence as a matrix by concatenating the joint coordinates in each instant and arranging those vector representations in a chronological order. Then the matrix is quantified into an image and normalized to handle the variable-length problem. The final image is fed into a CNN model for feature extraction and recognition. For the specific structure of such images, the simple max-pooling plays an important role on spatial feature selection as well as temporal frequency adjustment, which can obtain more discriminative joint information for different actions and meanwhile address the variable-frequency problem. Experimental results demonstrate that our method achieves the state-of-art performance with high computational efficiency, especially surpassing the existing result by more than 15 percentage on the challenging ChaLearn gesture recognition dataset.
KeywordSkeleton Based Action Recognition Convolutional Neural Network
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11696
Collection智能感知与计算研究中心
Corresponding AuthorWang, Liang
Affiliation1.Center for Research on Intelligent Perception and Computing, CRIPAC
2.Center for Excellence in Brain Science and Intelligence Technology, CEBSIT
3.Nat’l Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
4.College of Engineering, College of Computer and Information Science, Northeastern University, USA
Recommended Citation
GB/T 7714
Du, Yong,Fu, Yun,Wang, Liang. Skeleton Based Action Recognition with Convolutional Neural Network[C],2015.
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