Human Action Recognition using BlazePose Skeleton on Spatial Temporal Graph Convolutional Neural Networks

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Human Action Recognition using BlazePose Skeleton on Spatial Temporal Graph Convolutional Neural Networks

Alsawadi, MS; Rio, M; (2022) Human Action Recognition using BlazePose Skeleton on Spatial Temporal Graph Convolutional Neural Networks. In: Proceedings of the 9th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE) 2022. (pp. pp. 206-211). Institute of Electrical and Electronics Engineers (IEEE) Green open access

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Abstract

The trend in multimedia transmission in social media has increased tremendously during the last decade and it is expected to continue growing during the next. Therefore, the need for new tools with the capacity of analyzing this kind of data grows accordingly. In this work, we implement the BlazePose skeleton topology into the ST-GCN model for action recognition. We test our experiments on the UCF-101 and HMDB-51 datasets. These are the first experiments of action recognition using the BlazePose skeleton upon these benchmarks. Moreover, we present an improved skeleton topology based on BlazePose that can enhance the performance achieved by its predecessor. By using the Enhanced-BlazePose topology presented in this study, we improved the results of the ST-GCN model on the UCF-101 benchmark more than 13% in accuracy performance. Finally, we have released the BlazePose skeleton data of the UCF-101 and HMDB-51 from our experiments to contribute future studies in the research community.

Type:	Proceedings paper
Title:	Human Action Recognition using BlazePose Skeleton on Spatial Temporal Graph Convolutional Neural Networks
Event:	9th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE) 2022
Location:	Semarang, Indonesia
Dates:	25th-26th August 2022
ISBN-13:	9781665471480
Open access status:	An open access version is available from UCL Discovery
DOI:	10.1109/ICITACEE55701.2022.9924010
Publisher version:	https://doi.org/10.1109/ICITACEE55701.2022.9924010
Language:	English
Additional information:	This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
Keywords:	BlazePose, Skeleton, Action Recognition, Graph Neural Networks, Spatial-Temporal Graph Convolutional Networks
UCL classification:	UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI:	https://discovery.ucl.ac.uk/id/eprint/10164615