OATAO - Open Archive Toulouse Archive Ouverte Open Access Week

Learning to recognise 3D human action from a new skeleton-based representation using deep convolutional neural networks

Pham, Huy-Hieu and Khoudour, Louahdi and Crouzil, Alain and Zegers, Pablo and Velastin, Sergio A. Learning to recognise 3D human action from a new skeleton-based representation using deep convolutional neural networks. (2019) IET Computer Vision, 13 (3). 319-328. ISSN 1751-9632

[img]
Preview
(Document in English)

PDF (Author's version) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
1MB

Official URL: https://ieeexplore.ieee.org/document/8688703

Abstract

Recognising human actions in untrimmed videos is an important challenging task. An effective three-dimensional (3D) motion representation and a powerful learning model are two key factors influencing recognition performance. In this study, the authors introduce a new skeleton-based representation for 3D action recognition in videos. The key idea of the proposed representation is to transform 3D joint coordinates of the human body carried in skeleton sequences into RGB images via a colour encoding process. By normalising the 3D joint coordinates and dividing each skeleton frame into five parts, where the joints are concatenated according to the order of their physical connections, the colour-coded representation is able to represent spatio-temporal evolutions of complex 3D motions, independently of the length of each sequence. They then design and train different deep convolutional neural networks based on the residual network architecture on the obtained image-based representations to learn 3D motion features and classify them into classes. Their proposed method is evaluated on two widely used action recognition benchmarks: MSR Action3D and NTU-RGB+D, a very large-scale dataset for 3D human action recognition. The experimental results demonstrate that the proposed method outperforms previous state-of-the-art approaches while requiring less computation for training and prediction.

Item Type:Article
Additional Information:Thanks to IEEE editor. The definitive version is available at http://ieeexplore.ieee.org. The original PDF can be found at IET Computer Vision (ISSN 1751-9632) website : https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4159597
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > Aparnix (CHILE)
Other partners > Centre d'études et d'expertise sur les risques, l'environnement, la mobilité et l'aménagement - CEREMA (FRANCE)
Other partners > Cortexica vision (UNITED KINGDOM)
Other partners > Queen Mary University of London - QMUL (UNITED KINGDOM)
Other partners > Universidad Carlos III de Madrid - UC3M (SPAIN)
Laboratory name:
Statistics:download
Deposited On:11 Mar 2020 13:07

Repository Staff Only: item control page