Hierarchical recurrent neural network for skeleton based action recognition
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Yong Du | Liang Wang | Wei Wang | Liang Wang | Yong Du
[1] Kuldip K. Paliwal,et al. Bidirectional recurrent neural networks , 1997, IEEE Trans. Signal Process..
[2] Tido Röder,et al. Documentation Mocap Database HDM05 , 2007 .
[3] Ruzena Bajcsy,et al. Bio-inspired Dynamic 3D Discriminative Skeletal Features for Human Action Recognition , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops.
[4] Xi Chen,et al. Classifying and visualizing motion capture sequences using deep neural networks , 2013, 2014 International Conference on Computer Vision Theory and Applications (VISAPP).
[5] Gérard G. Medioni,et al. Structured Time Series Analysis for Human Action Segmentation and Recognition , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[6] Ying Wu,et al. Mining actionlet ensemble for action recognition with depth cameras , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[7] Ramakant Nevatia,et al. Recognition and Segmentation of 3-D Human Action Using HMM and Multi-class AdaBoost , 2006, ECCV.
[8] Nikos Nikolaidis,et al. Action recognition on motion capture data using a dynemes and forward differences representation , 2014, J. Vis. Commun. Image Represent..
[9] Alex Graves,et al. Practical Variational Inference for Neural Networks , 2011, NIPS.
[10] Alexandros André Chaaraoui,et al. A discussion on the validation tests employed to compare human action recognition methods using the MSR Action3D dataset , 2014, ArXiv.
[11] Ruzena Bajcsy,et al. Berkeley MHAD: A comprehensive Multimodal Human Action Database , 2013, 2013 IEEE Workshop on Applications of Computer Vision (WACV).
[12] Andrew W. Fitzgibbon,et al. Real-time human pose recognition in parts from single depth images , 2011, CVPR 2011.
[13] Yoshua Bengio,et al. Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies , 2001 .
[14] Jake K. Aggarwal,et al. View invariant human action recognition using histograms of 3D joints , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.
[15] Hairong Qi,et al. Group Sparsity and Geometry Constrained Dictionary Learning for Action Recognition from Depth Maps , 2013, 2013 IEEE International Conference on Computer Vision.
[16] Ling Shao,et al. Leveraging Hierarchical Parametric Networks for Skeletal Joints Based Action Segmentation and Recognition , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[17] Wojciech Zaremba,et al. Recurrent Neural Network Regularization , 2014, ArXiv.
[18] Jonathan Tompson,et al. Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation , 2014, NIPS.
[19] Qing Zhang,et al. A Survey on Human Motion Analysis from Depth Data , 2013, Time-of-Flight and Depth Imaging.
[20] R. Venkatesh Babu,et al. Real-time human action recognition from motion capture data , 2013, 2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG).
[21] Christian Wolf,et al. Sequential Deep Learning for Human Action Recognition , 2011, HBU.
[22] Venkatesh Babu Radhakrishnan,et al. Action recognition from motion capture data using Meta-Cognitive RBF Network classifier , 2014, 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP).
[23] Nasser Kehtarnavaz,et al. Real-time human action recognition based on depth motion maps , 2016, Journal of Real-Time Image Processing.
[24] Rama Chellappa,et al. Human Action Recognition by Representing 3D Skeletons as Points in a Lie Group , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[25] Samuel Berlemont,et al. BLSTM-RNN Based 3D Gesture Classification , 2013, ICANN.
[26] Jürgen Schmidhuber,et al. Evolving deep unsupervised convolutional networks for vision-based reinforcement learning , 2014, GECCO.
[27] James A. Reggia,et al. Robust human action recognition via long short-term memory , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).
[28] Yun Fu,et al. Prediction of Human Activity by Discovering Temporal Sequence Patterns , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[29] Alan L. Yuille,et al. An Approach to Pose-Based Action Recognition , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[30] Marwan Torki,et al. Histogram of Oriented Displacements (HOD): Describing Trajectories of Human Joints for Action Recognition , 2013, IJCAI.
[31] Geoffrey E. Hinton,et al. Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[32] Cristian Sminchisescu,et al. The Moving Pose: An Efficient 3D Kinematics Descriptor for Low-Latency Action Recognition and Detection , 2013, 2013 IEEE International Conference on Computer Vision.
[33] Ruzena Bajcsy,et al. Sequence of the Most Informative Joints (SMIJ): A new representation for human skeletal action recognition , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.
[34] Alex Graves,et al. Supervised Sequence Labelling with Recurrent Neural Networks , 2012, Studies in Computational Intelligence.
[35] Navdeep Jaitly,et al. Towards End-To-End Speech Recognition with Recurrent Neural Networks , 2014, ICML.
[36] Wanqing Li,et al. Action recognition based on a bag of 3D points , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.
[37] Xiaodong Yang,et al. Super Normal Vector for Activity Recognition Using Depth Sequences , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[38] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[39] A. Savitzky,et al. Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .