Technology of Graphic & Image
|
App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 12
3791-3795,3825

Skeleton recognition model based on enhanced graph convolution

Lan Hong
He Fan
Zhang Pufen
College of Information Engineering, Jiangxi University of Science & Technology, Ganzhou Jiangxi 341000, China
DOI: 10.19734/j.issn.1001-3695.2021.04.0154

Abstract

In order to solve the problem of extracting temporal, spatial and channel features, this paper proposed an ADGCN for skeleton action recognition. At first, it modeled the skeleton data and fed the movement of joints, bones and joints and bones information into the multi-stream framework of a single stream. And then it transferred the input data to the directed graph convolution network to extract the dependencies between joints and bones, after that, used the space-temporal channel attention network(STCN) to strengthen the key joints in each layer of network temporal, space and channels of information. The last, it calculated the accuracy of action recognition by the weighted average of the information of four streams, output action prediction results. This model trained and verified in two large data sets NTU-RGB+D and Kinectics-Skeleton. Compared with baseline DGNN(directed graph neural network), the accuracy of this paper on two cross subsets CS and CV is improved by 2.43% and 1.2%, on NTU-RGB+D dataset. On Kinectics-skeleton dataset, the accuracy of top1 and top5 is increased by 0.7% and 0.9%, respectively. The proposed ADGCN can effectively enhance the performance of skeleton action recognition, and the effect is improved on both large data sets.

Foundation Support

2020年江西省大学生创新基金资助项目

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.04.0154
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 12
Section: Technology of Graphic & Image
Pages: 3791-3795,3825
Serial Number: 1001-3695(2021)12-049-3791-05

Publish History

[2021-12-05] Printed Article

Cite This Article

兰红, 何璠, 张蒲芬. 基于增强型图卷积的骨架识别模型 [J]. 计算机应用研究, 2021, 38 (12): 3791-3795,3825. (Lan Hong, He Fan, Zhang Pufen. Skeleton recognition model based on enhanced graph convolution [J]. Application Research of Computers, 2021, 38 (12): 3791-3795,3825. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)