RT Conference Proceedings
T1 Learning and Recognizing Human Action from Skeleton Movement with Deep Residual Neural Networks
A1 Pham, Huy-Hieu
A1 Khoudour, Louahdi
A1 Crouzil, Alain
A1 Zegers, Pablo
A1 Velastin Carroza, Sergio Alejandro
AB 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
PB The Institution Of Engineering And Technology
SN 978-1-78561-652-5
YR 2017
FD 2017-07-11
LK https://hdl.handle.net/10016/28918
UL https://hdl.handle.net/10016/28918
LA eng
NO This paper has been presented at 8th International Conference of Pattern Recognition Systems (ICPRS 2017).
NO This work was supported by the Cerema Research Center and Universidad Carlos III de Madrid. Sergio A. Velastin has received funding from the European Unions Seventh Framework Programme for Research, Technological Development and demonstration under grant agreement No 600371, el Ministerio de Economía, Industria y Competitividad (COFUND2013-51509) el Ministerio de Educación, cultura y Deporte (CEI-15-17) and Banco Santander.
DS e-Archivo
RD 1 jun. 2024