Pham, Huy-HieuKhoudour, LouahdiCrouzil, AlainZegers, PabloVelastin Carroza, Sergio Alejandro2019-09-302019-09-302017-07-11Pham, H.H., Khoudour, L., Crouzil, A., Zegers, P. y Velastin, S.A. (2017). Learning and recognizing human action from Skeleton movement with deep residual neural networks. In 8th ​​International Conference of Pattern Recognition Systems (ICPRS 2017).978-1-78561-652-5https://hdl.handle.net/10016/28918This paper has been presented at 8th International Conference of Pattern Recognition Systems (ICPRS 2017).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 approaches6eng© 2017 IEEE.Action recognitionResNetSkeletonKinectconference paperInformáticahttps://doi.org/10.1049/cp.2017.0154open access8th International Conference of Pattern Recognition Systems (ICPRS 2017)CC/0000027464