In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) will be discontinued after Dec. 31st, 2024.
See More
Technology of Graphic & Image
|
App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 10
3107-3111

Improved deep convolutional neural network for human action recognition

He Bingqiana
Wei Weib
Zhang Bina
Gao Lianxina
Song Yanbeia
a. School of Computer Science, b. School of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China
DOI: 10.19734/j.issn.1001-3695.2018.06.0361

Abstract

Aiming at the problem that the existing human motion recognition method needed to input a fixed length video segment and underutilized the spatio-temporal information, this paper proposed a deep neural network model based on the combination of space-time pyramid and attention mechanism. This improved architecture combined 3D-CNN including spatio-temporal pyramids with LSTM model with spatio-temporal attention mechanism, and realized multi-scale processing of video segments and full utilization of complex spatio-temporal information of actions. For the architecture, the inputs of spatial and temporal domain were RGB image and the optical flow, the input of the fusion domain was the fusion feature of the motion and appearance features of the pyramid pooling layer. Finally, it used the decision fusion strategy to obtain the final motion recognition result. Experiments were performed on the UCF101 and HMDB51 datasets, it achieved 94.2% and 70.5% recognition accuracy, respectively. The experimental results show that the improved network model achieves high recognition accuracy in video based human motion recognition tasks.

Foundation Support

四川省教育厅重点科研资助项目(17ZA0064)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.06.0361
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 10
Section: Technology of Graphic & Image
Pages: 3107-3111
Serial Number: 1001-3695(2019)10-050-3107-05

Publish History

[2019-10-05] Printed Article

Cite This Article

何冰倩, 魏维, 张斌, 等. 基于改进的深度神经网络的人体动作识别模型 [J]. 计算机应用研究, 2019, 36 (10): 3107-3111. (He Bingqian, Wei Wei, Zhang Bin, et al. Improved deep convolutional neural network for human action recognition [J]. Application Research of Computers, 2019, 36 (10): 3107-3111. )

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)