Human Action Recognition Based on Skeleton Features

Yi Gao1, Haitao Wu1, Xinmeng Wu1, Zilin Li1 and Xiaofan Zhao2

  1. College of Intelligence and Computing
    Tianjin University, Tianjin, China
  2. School of Information Technology and Cyber Security
    People’s Public Security University of China, Beijing, China


Based on human bone joints, skeleton information has clear and simple features and is not easily affected by appearance factors. In this paper, an improved feature of Gist, ExGist, is proposed to describe the skeleton information of human bone joints for human action recognition. The joint coordinates are extracted by using OpenPose and the thermodynamic diagram, and ExGist is used for feature extraction. The advantage of ExGist is that it can effectively characterize the local and global features of skeleton information while maintaining the original advantages of Gist feature. Compared with Gist, ExGist achieves better results on different classifiers. Additionally, compared with C3D and APTNet, our model also obtains better results with an accuracy rate of 89.2%.

Key words

Human Action Recognition, Gist, OpenPose, Euclidean Distance, Thermodynamic Diagram

Digital Object Identifier (DOI)

Publication information

Volume 14, Issue 3 (September 2017)
Advances in Information Technology, Distributed and Model Driven Systems
Year of Publication: 2017
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Gao, Y., Wu, H., Wu, X., Li, Z., Zhao, X.: Human Action Recognition Based on Skeleton Features. Computer Science and Information Systems, Vol. 14, No. 3, 537–550. (2017),