Skeletal Keypoint-Based Transformer Model for Human Action Recognition in Aerial Videos

UDDIN, Shahab, NAWAZ, Tahir, FERRYMAN, James, RASHID, Nasir, ASADUZZAMAN, Md and NAWAZ, Raheel (2024) Skeletal Keypoint-Based Transformer Model for Human Action Recognition in Aerial Videos. IEEE Access, 12. ISSN 2169-3536

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Abstract or description

Several efforts have been made to develop effective and robust vision-based solutions for human aerial action recognition. Generally, the existing methods rely on the extraction of either spatial features (patch-based methods) or skeletal key points (pose-based methods) that are fed to a classifier. The pose-based methods are generally regarded to be more robust to background changes and computationally efficient. Moreover, at the classification stage, the use of deep networks has generated significant interest within the community; however, the need remains to develop accurate and computationally effective deep learning-based solutions. To this end, this paper proposes a lightweight Transformer network-based method for human action recognition in aerial videos using the skeletal keypoints extracted with YOLOv8. The effectiveness of the proposed method is shown on a well-known public dataset containing 13 action classes, achieving very encouraging performance in terms of accuracy and computational cost as compared to several existing related methods.

Item Type:	Article
Uncontrolled Keywords:	Action recognition, transformer network, aerial videos, video surveillance.
Faculty:	School of Digital, Technologies and Arts > Engineering
Depositing User:	Md ASADUZZAMAN
Date Deposited:	06 Feb 2024 16:55
Last Modified:	06 Feb 2024 16:55
URI:	https://eprints.staffs.ac.uk/id/eprint/8079

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