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Review

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; https://doi.org/10.3390/s21124246
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
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MDPI and ACS Style

Shaikh, M.B.; Chai, D. RGB-D Data-Based Action Recognition: A Review. Sensors 2021, 21, 4246. https://doi.org/10.3390/s21124246

AMA Style

Shaikh MB, Chai D. RGB-D Data-Based Action Recognition: A Review. Sensors. 2021; 21(12):4246. https://doi.org/10.3390/s21124246

Chicago/Turabian Style

Shaikh, Muhammad B., and Douglas Chai. 2021. "RGB-D Data-Based Action Recognition: A Review" Sensors 21, no. 12: 4246. https://doi.org/10.3390/s21124246

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