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Learning and Recognizing Human Action from Skeleton Movement with Deep Residual Neural Networks

Pham, Huy-Hieu and Khoudour, Louahdi and Crouzil, Alain and Zegers, Pablo and Velastin, Sergio A. Learning and Recognizing Human Action from Skeleton Movement with Deep Residual Neural Networks. (2017) In: ICPRS 2017: 8th International Conference of Pattern Recognition Systems, 11 July 2017 - 13 July 2017 (Madrid, Spain).

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Official URL: https://doi.org/10.1049/cp.2017.0154

Abstract

Automatic human action recognition is indispensable for almost artificial intelligent systems such as video surveillance, human-computer interfaces, video retrieval, etc. Despite a lot of progresses, recognizing actions in a unknown video is still a challenging task in computer vision. Recently, deep learning algorithms has proved its great potential in many vision-related recognition tasks. In this paper, we propose the use of Deep Residual Neural Networks (ResNets) to learn and recognize human action from skeleton data provided by Kinect sensor. Firstly, the body joint coordinates are transformed into 3D-arrays and saved in RGB images space. Five different deep learning models based on ResNet have been designed to extract image features and classify them into classes. Experiments are conducted on two public video datasets for human action recognition containing various challenges. The results show that our method achieves the state-of-the-art performance comparing with existing approaches.

Item Type:Conference or Workshop Item (Paper)
Audience (conference):International conference proceedings
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 > Centre d'études et d'expertise sur les risques, l'environnement, la mobilité et l'aménagement - CEREMA (FRANCE)
Other partners > Universidad de Los Andes (CHILE)
Other partners > Universidad Carlos III de Madrid - UC3M (SPAIN)
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Deposited On:09 Jun 2020 08:39

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