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)
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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 |
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