Human Action Recognition: A Taxonomy-Based Survey, Updates, and Opportunities

Human action recognition systems use data collected from a wide range of sensors to accurately identify and interpret human actions. One of the most challenging issues for computer vision is the automatic and precise identification of human activities. A significant increase in feature learning-base...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 23; no. 4; p. 2182
Main Authors: Morshed, Md Golam, Sultana, Tangina, Alam, Aftab, Lee, Young-Koo
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 15.02.2023
MDPI
Subjects:
Algorithms
Artificial intelligence
Augmented Reality
Automation
Biometrics
Cameras
Computer Security
Computer vision
Datasets
Deep learning
Forecasts and trends
Hand
hand-crafted
human action recognition
Human Activities
Human acts
Human behavior
Humans
Identification
Learning strategies
Machine learning
Machine vision
Neural networks
Pattern Recognition, Automated
Performance evaluation
Research methodology
Review
Sensors
Surveillance
survey
Surveys
Taxonomy
Trends
Virtual reality
United States
New Jersey
Germany
deep learning
survey
human action recognition
hand-crafted
taxonomy
computer vision
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:Human action recognition systems use data collected from a wide range of sensors to accurately identify and interpret human actions. One of the most challenging issues for computer vision is the automatic and precise identification of human activities. A significant increase in feature learning-based representations for action recognition has emerged in recent years, due to the widespread use of deep learning-based features. This study presents an in-depth analysis of human activity recognition that investigates recent developments in computer vision. Augmented reality, human–computer interaction, cybersecurity, home monitoring, and surveillance cameras are all examples of computer vision applications that often go in conjunction with human action detection. We give a taxonomy-based, rigorous study of human activity recognition techniques, discussing the best ways to acquire human action features, derived using RGB and depth data, as well as the latest research on deep learning and hand-crafted techniques. We also explain a generic architecture to recognize human actions in the real world and its current prominent research topic. At long last, we are able to offer some study analysis concepts and proposals for academics. In-depth researchers of human action recognition will find this review an effective tool.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s23042182