Identifying Human Actions using Convolutional Neural Networks

Authors

  • Abdurashidova Kamola Turgunbaevna Associate professor at Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Department of Computer Systems
  • Abdukhakimov Fayzulla Kudratulla ugli Graduate student at Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Department of Computer Systems
  • Chorshanbiyeva Sevinch Akramovna Undergraduate student at Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Department of Computer Systems

Keywords:

CNN, feature engineering, spatial-temporal activity, poses, pooling

Abstract

Video indexing, intelligent surveillance, multimedia understanding, and other domains all make extensive use of video action recognition. Lately, it was significantly enhanced by adding deep learning through Convolutional Neural Network (CNN) learning. This inspired us to examine the noteworthy efforts on action recognition using CNN. This paper presents a clear and objective overview of CNN-based action recognition and offers recommendations for further research.

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Published

2024-04-30

Issue

Vol. 2 No. 4 (2024): American Journal of Engineering , Mechanics and Architecture

Section

Articles

How to Cite

Identifying Human Actions using Convolutional Neural Networks. (2024). American Journal of Engineering , Mechanics and Architecture (2993-2637), 2(4), 153-158. https://grnjournal.us/index.php/AJEMA/article/view/4443