Abstract
Classification of human actions is an ongoing research problem in computer vision. This review is aimed to scope current literature on data fusion and action recognition techniques and to identify gaps and future research direction. Success in producing cost-effective and portable vision-based sensors has dramatically increased the number and size of datasets. The increase in the number of action recognition datasets intersects with advances in deep learning architectures and computational support, both of which offer significant research opportunities. Naturally, each action-data modality—such as RGB, depth, skeleton, and infrared (IR)—has distinct characteristics; therefore, it is important to exploit the value of each modality for better action recognition. In this paper, we focus solely on data fusion and recognition techniques in the context of vision with an RGB-D perspective. We conclude by discussing research challenges, emerging trends, and possible future research directions.
RAS ID
39749
Document Type
Journal Article
Date of Publication
2021
Volume
21
Issue
12
Funding Information
Edith Cowan University - Open Access Support Scheme 2021
Edith Cowan University / Higher Education Commission, Pakistan
School
School of Engineering / Graduate Research / Strategic and Governance Services
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Publisher
MDPI
Identifier
Muhammad Bilal Shaikh
ORCID : 0000-0001-9042-5018
Douglas Chai
ORCID : 0000-0002-9004-7608
Comments
Shaikh, M. B., & Chai, D. (2021). RGB-D data-based action recognition: A review. Sensors, 21(12), article 4246. https://doi.org/10.3390/s21124246