Cited by 3,965Human Pose and Action RecognitionHand Gesture Recognition SystemsContext-Aware Activity Recognition Systems

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HMDB: A large video database for human motion recognition

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Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., Serre, T.·2011·2011 International Conference on Computer Vision

DOI 10.1109/iccv.2011.6126543

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Abstract

With nearly one billion online videos viewed everyday, an emerging new frontier in computer vision research is recognition and search in video. While much effort has been devoted to the collection and annotation of large scalable static image datasets containing thousands of image categories, human action datasets lag far behind. Current action recognition databases contain on the order of ten different action categories collected under fairly controlled conditions. State-of-the-art performance on these datasets is now near ceiling and thus there is a need for the design and creation of new benchmarks. To address this issue we collected the largest action video database to-date with 51 action categories, which in total contain around 7,000 manually annotated clips extracted from a variety of sources ranging from digitized movies to YouTube. We use this database to evaluate the performance of two representative computer vision systems for action recognition and explore the robustness of these methods under various conditions such as camera motion, viewpoint, video quality and occlusion.

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Authors: Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., & Serre, T.
Affiliations: Karlsruhe Institute of Technology (DE)
Massachusetts Institute of Technology (US)
Brown University (US)
ORCID: Kuehne, H.
Garrote, E.
Poggio, T.
Venue: 2011 International Conference on Computer Vision
Publisher: IEEE
Published: 2011-11
Pages: 2556-2563
DOI: 10.1109/iccv.2011.6126543
OpenAlex: W2126579184
Semantic Scholar: 8b3b8848a311c501e704c45c6d50430ab7068956
Cited by: 3,965
References: 31
Citations by year: 2026: 164 · 2025: 339 · 2024: 412 · 2023: 432 · 2022: 389 · 2021: 476 · 2020: 393 · 2019: 359 · 2018: 272 · 2017: 213 · 2016: 193 · 2015: 145 · 2014: 85 · 2013: 67 · 2012: 25
FWCI: 41.79
Type: conference paper
Language: en
Open access: green
hdl.handle.net
Licence: cc-by-nc-sa
Locations: IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
TECNALIA Publications (Fundación TECNALIA Research & Innovation) (submittedVersion, public-domain)
DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
Funders: National Science Foundation (0827427, 0640097)
Brown University
Defense Advanced Research Projects Agency
Office of Naval Research
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
Keywords: human action recognition, HMDB, benchmark dataset, video annotation, occlusion robustness, viewpoint variation, camera movement, video quality, YouTube videos

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HMDB: A large video database for human motion recognition

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