HMDB: A large video database for human motion recognition

Keywords

Computer science, Artificial intelligence, Motion (physics), Computer vision, Human motion

References

• Contributions of form, motion and task to biological motion perception.道夫 花木 — 2009
• Perceiving events and objectsGunnar Jansson — 2013
• Modeling the Shape of the Scene: A Holistic Representation of the Spatial EnvelopeAude Oliva, Antonio Torralba — 2001
• Evaluation of Local Spatio-temporal Features for Action RecognitionHeng Wang, Muhammad Muneeb Ullah, Alexander Kläser — 2009
• Actions as space-time shapesMiri Blank, Lena Gorelick, Eli Shechtman — 2005
• SUN database: Large-scale scene recognition from abbey to zooJianxiong Xiao, James Hays, Krista A. Ehinger — 2010
• On Space-Time Interest PointsIvan Laptev — 2005
• Grouplet: A structured image representation for recognizing human and object interactionsBangpeng Yao, Li Fei-Fei — 2010
• A model of neuronal responses in visual area MT.Eero P. Simoncelli, David J. Heeger — 1998
• LabelMe: Online Image Annotation and ApplicationsAntonio Torralba, Bryan Russell, Jenny Yuen — 2010
• A Biologically Inspired System for Action RecognitionHueihan Jhuang, T. Serre, Lior Wolf — 2007
• A survey of vision-based methods for action representation, segmentation and recognitionDaniel Weinland, Rémi Ronfard, Edmond Boyer — 2011
• Automated home-cage behavioural phenotyping of mice.Hueihan Jhuang, Estíbaliz Garrote, Xinlin Yu — 2010
• Recognizing realistic actions from videos “in the wild”Jingen Liu, Jiebo Luo, Mubarak Shah — 2009
• Action MACH a spatio-temporal Maximum Average Correlation Height filter for action recognitionMikel Rodríguez, Javed Ahmed, Mubarak Shah — 2008
• ImageNet: A large-scale hierarchical image databaseJia Deng, Wei Dong, Richard Socher — 2009
• LabelMe: A Database and Web-Based Tool for Image AnnotationBryan Russell, Antonio Torralba, Kevin Murphy — 2008
• Action snippets: How many frames does human action recognition require?Stefano D’Aronco, Luc van Gool — 2008
• Learning realistic human actions from moviesIvan Laptev, Marcin Marszałek, Cordelia Schmid — 2008
• Robust Object Recognition with Cortex-Like MechanismsT. Serre, Lior Wolf, Stanley Bileschi — 2007

Cited by

• UCF101: A Dataset of 101 Human Actions Classes From Videos in The WildKhurram Soomro, Amir Zamir, Mubarak Shah — 2012
• Employing automatic content recognition for teaching methodology analysis in classroom videosMonique Dorizon — 2022
• A Study on Unsupervised Dictionary Learning and Feature Encoding for Action ClassificationXiaojiang Peng, Qiang Peng, Yu Qiao — 2013
• Modèles structurés pour la reconnaissance d'actions dans des vidéos réalistesAdrien Gaidon — 2012
• Reconnaissance d'actions en temps réel à partir d'exemples. (Real time actions recognition from examplars)Mathieu Barnachon — 2013
• Feature sampling and partitioning for visual vocabulary generation on large action classification datasetsMichael Sapienza, Fabio Cuzzolin, Philip H. S. Torr — 2014
• The Johns Hopkins University multimodal dataset for human action recognitionThomas S. Murray, Daniel R. Mendat, Philippe O. Pouliquen — 2015
• Temporal Extension of Scale Pyramid and Spatial Pyramid Matching for Action RecognitionZhenzhong Lan, Xuanchong Li, Alexandar G. Hauptmann — 2014
• MSEE: Stochastic Cognitive Linguistic Behavior Models for Semantic SensingHong Man, Yueyue Yao — 2013
• Feature Quantization and Pooling for VideosEkaterina H. Taralova — 2014
• Reconnaissance d'activités humaines à partir de séquences vidéo. (Human activity recognition from video sequences)Mouna Selmi — 2014
• Beyond Temporal Pooling: Recurrence and Temporal Convolutions for Gesture Recognition in VideoLionel Pigou, Aäron van den Oord, Sander Dieleman — 2015
• Unsupervised Semantic Parsing of Video CollectionsOzan Şener, Amir Zamir, Silvio Savarese — 2015
• Enhanced image and video representation for visual recognitionMihir Jain — 2014
• Supervised Learning Approaches for Automatic Structuring of Videos. (Méthodes d'apprentissage supervisé pour la structuration automatique de vidéos)Danila Potapov — 2015
• Recognizing human actions by two-level Beta process hidden Markov modelLu Lu, Yiju Zhan, Qing Jiang — 2017
• Human action recognition based on multi-layer Fisher vector encoding methodManel Sekma, Mahmoud Mejdoub, Chokri Ben Amar — 2015
• A Robust and Efficient Video Representation for Action RecognitionHeng Wang, Dan Oneaţă, Jakob Verbeek — 2015
• The INRIA-LIM-VocR and AXES submissions to TrecVid 2014 Multimedia Event DetectionMatthijs Douze, Dan Oneaţă, Mattis Paulin — 2014
• The Best of BothWorlds: Combining Data-Independent and Data-Driven Approaches for Action RecognitionZhenzhong Lan, Shoou-I Yu, Dezhong Yao — 2015

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