Alsawadi, MS;
El-Kenawy, ESM;
Rio, M;
(2022)
Using BlazePose on Spatial Temporal Graph Convolutional Networks for Action Recognition.
Computers, Materials and Continua
, 74
(1)
pp. 19-36.
10.32604/cmc.2023.032499.
Preview |
Text
M_Alsawadi_Paper3_TSP_CMC_32499.pdf - Published Version Download (1MB) | Preview |
Abstract
The ever-growing available visual data (i.e., uploaded videos and pictures by internet users) has attracted the research community’s attention in the computer vision field. Therefore, finding efficient solutions to extract knowledge from these sources is imperative. Recently, the BlazePose system has been released for skeleton extraction from images oriented to mobile devices. With this skeleton graph representation in place, a Spatial-Temporal Graph Convolutional Network can be implemented to predict the action. We hypothesize that just by changing the skeleton input data for a different set of joints that offers more information about the action of interest, it is possible to increase the performance of the Spatial-Temporal Graph Convolutional Network for HAR tasks. Hence, in this study, we present the first implementation of the BlazePose skeleton topology upon this architecture for action recognition. Moreover, we propose the Enhanced-BlazePose topology that can achieve better results than its predecessor. Additionally, we propose different skeleton detection thresholds that can improve the accuracy performance even further. We reached a top-1 accuracy performance of 40.1% on the Kinetics dataset. For the NTU-RGB+D dataset, we achieved 87.59% and 92.1% accuracy for Cross-Subject and Cross-View evaluation criteria, respectively.
| Type: | Article |
|---|---|
| Title: | Using BlazePose on Spatial Temporal Graph Convolutional Networks for Action Recognition |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.32604/cmc.2023.032499 |
| Publisher version: | https://doi.org/10.32604/cmc.2023.032499 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Action recognition; BlazePose; graph neural network; OpenPose; skeleton; spatial temporal graph convolution 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/10157991 |
Archive Staff Only
 |
View Item |
