Linear latent low dimensional space for online early action recognition and prediction, Pattern Recognition, vol.72, pp.532-547, 2017. ,
DOI : 10.1016/j.patcog.2017.07.003
A na??ve Bayes baseline for early gesture recognition, Pattern Recognition Letters, vol.73, pp.91-99, 2016. ,
DOI : 10.1016/j.patrec.2016.01.013
Sequential Max-Margin Event Detectors, Proceedings of the European conference on computer vision, pp.410-424, 2014. ,
DOI : 10.1007/978-3-319-10578-9_27
, Mocap database hdm05, p.7, 2007.
Instructing people for training gestural interactive systems, Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems, CHI '12, pp.1737-1746, 2012. ,
DOI : 10.1145/2207676.2208303
Hierarchical recurrent neural network for skeleton based action recognition, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.1110-1118, 2015. ,
Co-occurrence feature learning for skeleton based action recognition using regularized deep lstm networks, Proceedings of the 30th AAAI Conference on Artificial Intelligence, p.8, 2016. ,
NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1010-1019, 2016. ,
DOI : 10.1109/CVPR.2016.115
Action points : A representation for low-latency online human action recognition Microsoft Research Cambridge, 2012. ,
HIF3D: Handwriting-Inspired Features for 3D skeleton-based action recognition, 2016 23rd International Conference on Pattern Recognition (ICPR), pp.985-990, 2016. ,
DOI : 10.1109/ICPR.2016.7899764
URL : https://hal.archives-ouvertes.fr/hal-01376113
LIBSVM, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.3, p.27, 2011. ,
DOI : 10.1145/1961189.1961199
Classifying and visualizing motion capture sequences using deep neural networks, Proceedings of the IEEE International Conference on Computer Vision Theory and Applications, pp.122-130, 2014. ,