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Pixel Convolutional Networks for Skeleton-Based Human Action Recognition

  • Zhichao ChangEmail author
  • Jiangyun Wang
  • Liang Han
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 946)

Abstract

Human action recognition is an important field in computer vision. Skeleton-based models of human obtain more attention in related researches because of strong robustness to external interference factors. In traditional researches the form of the feature is usually so hand-crafted that effective feature is difficult to extract from skeletons. In this paper a unique method is proposed for human action recognition called Pixel Convolutional Networks, which use a natural and intuitive way to extract skeleton feature from two dimensions, space and time. It achieves good performance compared with mainstream methods in the past few years in the large dataset NTU-RGB+D.

Keywords

Human action recognition Skeleton-based models Skeleton pixel pictures Pixel convolutional networks 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.School of Automation Science and Electrical EngineeringBeihang UniversityBeijingChina

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