Next Article in Journal
A New Fracture Detection Algorithm of Low Amplitude Acoustic Emission Signal Based on Kalman Filter-Ripple Voltage
Next Article in Special Issue
Sea Fog Dissipation Prediction in Incheon Port and Haeundae Beach Using Machine Learning and Deep Learning
Previous Article in Journal
Wireless Body Area Network Control Policies for Energy-Efficient Health Monitoring
Previous Article in Special Issue
SAFS: Object Tracking Algorithm Based on Self-Adaptive Feature Selection

RGB-D Data-Based Action Recognition: A Review

School of Engineering, Edith Cowan University, Perth, WA 6027, Australia
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Xue-Bo Jin
Sensors 2021, 21(12), 4246;
Received: 28 March 2021 / Revised: 9 June 2021 / Accepted: 9 June 2021 / Published: 21 June 2021
(This article belongs to the Collection Multi-Sensor Information Fusion)
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. View Full-Text
Keywords: action recognition; deep learning; data fusion; RGB-D action recognition; deep learning; data fusion; RGB-D
Show Figures

Figure 1

MDPI and ACS Style

Shaikh, M.B.; Chai, D. RGB-D Data-Based Action Recognition: A Review. Sensors 2021, 21, 4246.

AMA Style

Shaikh MB, Chai D. RGB-D Data-Based Action Recognition: A Review. Sensors. 2021; 21(12):4246.

Chicago/Turabian Style

Shaikh, Muhammad B., and Douglas Chai. 2021. "RGB-D Data-Based Action Recognition: A Review" Sensors 21, no. 12: 4246.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop