Alsawadi, Motasem S.;
Rio, Miguel;
(2022)
Skeleton-Split Framework using Spatial Temporal Graph Convolutional Networks for Action Recognition.
In:
Proceedings of the 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART) 2021.
(pp. pp. 1-5).
Institute of Electrical and Electronics Engineers (IEEE)
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Abstract
There has been a dramatic increase in the volume of videos and their related content uploaded to the internet. Accordingly, the need for efficient algorithms to analyse this vast amount of data has attracted significant research interest. This work aims to recognize activities of daily living using the ST-GCN model, providing a comparison between four different partitioning strategies: spatial configuration partitioning, full distance split, connection split, and index split. To achieve this aim, we present the first implementation of the ST-GCN framework upon the HMDB-51 dataset. Additionally, we show that our proposals have achieved the highest accuracy performance on the UCF-101 dataset using the ST-GCN framework than the state-of-the-art approach.
| Type: | Proceedings paper |
|---|---|
| Title: | Skeleton-Split Framework using Spatial Temporal Graph Convolutional Networks for Action Recognition |
| Event: | The 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART) 2021 |
| Location: | Paris (Créteil), France |
| Dates: | 8th-10th December 2021 |
| ISBN-13: | 978-1-6654-0810-3 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/BioSMART54244.2021.9677634 |
| Publisher version: | https://doi.org/10.1109/BioSMART54244.2021.9677634 |
| 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: | Spatial Temporal Graph Convolution Network, Skeleton, HMDB-51, UCF-101, Action Recognition, Graph Neural Network |
| UCL classification: | 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 UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10152122 |
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