IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Reliability Enhancement Algorithm of Human Motion Recognition Based on Knowledge Graph

Reliability Enhancement Algorithm of Human Motion Recognition Based on Knowledge Graph
View Sample PDF
Author(s): Yongwei Wang (Capital Normal University, China)and Feng Feng (Capital Normal University, China)
Copyright: 2021
Volume: 12
Issue: 1
Pages: 15
Source title: International Journal of Distributed Systems and Technologies (IJDST)
Editor(s)-in-Chief: Nik Bessis (Edge Hill University, UK)
DOI: 10.4018/IJDST.2021010101

Purchase

View Reliability Enhancement Algorithm of Human Motion Recognition Based on Knowledge Graph on the publisher's website for pricing and purchasing information.

Abstract

In order to solve the problem of uneven spatial distribution of human motion image and low peak signal-to-noise ratio (PSNR) of image reliability enhancement, a reliability enhancement algorithm for human motion recognition based on knowledge graph is proposed. An automatic spatial planning model of human motion image is constructed. The human motion spatial features are sampled, and the three-dimensional contour feature reconstruction model is established. The human motion spatial contour structure is reconstructed by adaptive edge feature detection method, and the knowledge graph of the motion image is extracted. Multi-scale information enhancement method is used to enhance and recognize the reliability of human motion image. The experimental results show that the method has the advantages of good reliability, high signal-to-noise ratio of image enhancement, and high accuracy of human motion recognition.

Related Content

Honglong Xu, Zhonghao Liang, Kaide Huang, Guoshun Huang, Yan He. © 2024. 17 pages.
Sherin Eliyas, P. Ranjana. © 2024. 10 pages.
Shuang Li, Xiaoguo Yao. © 2024. 16 pages.
Jialan Sun. © 2024. 21 pages.
Mei Gong, Bingli Mo. © 2024. 15 pages.
Qian He, Ke Wang. © 2024. 19 pages.
Sunil Kumar, Rashmi Mishra, Tanvi Jain, Achyut Shankar. © 2024. 12 pages.
Body Bottom