Hierarchical recurrent neural network for skeleton based action recognition

Keywords

Skeleton (computer programming), Computer science, Recurrent neural network, Human skeleton, Artificial intelligence

References

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• Recurrent Neural Network RegularizationWojciech Zaremba, Ilya Sutskever, Oriol Vinyals — 2014
• A discussion on the validation tests employed to compare human action recognition methods using the MSR Action3D datasetJosé Ramón Padilla López, Alexandros André Chaaraoui, Francisco Flórez‐Revuelta — 2014
• Classifying and visualizing motion capture sequences using deep neural networksKyunghyun Cho, Xi Chen — 2013
• Sequence of the Most Informative Joints (SMIJ): A new representation for human skeletal action recognitionFerda Ofli, Rizwan Chaudhry, Gregorij Kurillo — 2012
• The Moving Pose: An Efficient 3D Kinematics Descriptor for Low-Latency Action Recognition and DetectionMihai Zanfir, Marius Leordeanu, Cristian Sminchisescu — 2013
• Leveraging Hierarchical Parametric Networks for Skeletal Joints Based Action Segmentation and RecognitionDi Wu, Ling Shao — 2014
• Action recognition on motion capture data using a dynemes and forward differences representationIoannis Kapsouras, Nikos Nikolaidis — 2014
• Robust human action recognition via long short-term memoryAlexander Grushin, Derek Monner, James A. Reggia — 2013
• Action recognition from motion capture data using Meta-Cognitive RBF Network classifierSuraj Vantigodi, Venkatesh Babu Radhakrishnan — 2014
• Bio-inspired Dynamic 3D Discriminative Skeletal Features for Human Action RecognitionRizwan Chaudhry, Ferda Ofli, Gregorij Kurillo — 2013
• Human Action Recognition by Representing 3D Skeletons as Points in a Lie GroupRaviteja Vemulapalli, Felipe Arrate, Rama Chellappa — 2014
• Real-time human pose recognition in parts from single depth imagesJamie Shotton, Toby Sharp, Alex Kipman — 2011
• Long Short-Term MemorySepp Hochreiter, Jürgen Schmidhuber — 1997
• Real-time human action recognition based on depth motion mapsChen Chen, Kui Liu, Nasser Kehtarnavaz — 2013
• Berkeley MHAD: A comprehensive Multimodal Human Action DatabaseFerda Ofli, Rizwan Chaudhry, Gregorij Kurillo — 2013
• Real-time human action recognition from motion capture dataSuraj Vantigodi, R. Venkatesh Babu — 2013
• Super Normal Vector for Activity Recognition Using Depth SequencesXiaodong Yang, Yingli Tian — 2014
• Prediction of Human Activity by Discovering Temporal Sequence PatternsKang Li, Yun Fu — 2014
• Structured Time Series Analysis for Human Action Segmentation and RecognitionDian Gong, Gérard Medioni, Xuemei Zhao — 2014

Cited by

• An interactive and low-cost full body rehabilitation framework based on 3D immersive serious gamesFreda-Jean Meltz — 2019
• Jointly Learning Heterogeneous Features for RGB-D Activity RecognitionJian–Fang Hu, Wei‐Shi Zheng, Jianhuang Lai — 2015
• Deep Gaussian Conditional Random Field Network: A Model-Based Deep Network for Discriminative DenoisingRaviteja Vemulapalli, Oncel Tuzel, Ming-Yu Liu — 2015
• Semi-supervised Learning with Encoder-Decoder Recurrent Neural Networks: Experiments with Motion Capture SequencesFélix G. Harvey, Christopher Pal — 2015
• Recurrent Neural Networks for driver activity anticipation via sensory-fusion architectureAshesh Jain, Avi Singh, Hema Swetha Koppula — 2015
• Structural-RNN: Deep Learning on Spatio-Temporal GraphsAshesh Jain, Amir Zamir, Silvio Savarese — 2015
• A Review of Human Activity Recognition MethodsMichalis Vrigkas, Christophoros Nikou, Ioannis A. Kakadiaris — 2015
• Rank Pooling for Action RecognitionBasura Fernando, Efstratios Gavves, José Oramas — 2015
• Brain4Cars: Car That Knows Before You Do via Sensory-Fusion Deep Learning ArchitectureAshesh Jain, Hema Swetha Koppula, Shane Soh — 2016
• Space-Time Representation of People Based on 3D Skeletal Data: A ReviewFei Han, Brian Reily, William Hoff — 2016
• Moving Poselets: A Discriminative and Interpretable Skeletal Motion Representation for Action RecognitionLingling Tao, René Vidal — 2015
• Learning Contextual Dependence With Convolutional Hierarchical Recurrent Neural NetworksZhen Zuo, Bing Shuai, Gang Wang — 2015
• Human action recognition using multi-layer codebooks of key poses and atomic motionsGuangming Zhu, Liang Zhang, Peiyi Shen — 2016
• RGB-D-based action recognition datasets: A surveyJing Zhang, Wanqing Li, Philip Ogunbona — 2016
• Deep Multimodal Feature Analysis for Action Recognition in RGB+D VideosAmir Shahroudy, Tian-Tsong Ng, Yihong Gong — 2016
• Representation Learning of Temporal Dynamics for Skeleton-Based Action RecognitionYong Du, Yun Fu, Liang Wang — 2016
• Classification of human actions using pose-based features and stacked auto encoderEarnest Paul Ijjina, C. Krishna Mohan — 2016
• Discriminative Relational Representation Learning for RGB-D Action RecognitionYu Kong, Yun Fu — 2016
• R3DG features: Relative 3D geometry-based skeletal representations for human action recognitionRaviteja Vemulapalli, Felipe Arrate, Rama Chellappa — 2016
• Latent Max-Margin Multitask Learning With Skelets for 3-D Action RecognitionYanhua Yang, Cheng Deng, Dapeng Tao — 2017

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