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HMDB: A large video database for human motion recognition
Open accessAbstract
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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Spatiotemporal Feature Residual Propagation for Action Prediction
Zhao, H., & Wildes, R. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00710 · Cited by 36
[Retracted] Dance Movement Recognition Based on Feature Expression and Attribute Mining
Zhai, X. (2021) · Complexity · doi:10.1155/2021/9935900 · Cited by 36
RetractedDong, X., Zhao, Z., Wang, Y., Zeng, T., Wang, J., & Sui, Y. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3122332 · Cited by 38
Human Action Recognition Based on Improved Two-Stream Convolution Network
Wang, Z., Lu, H., Jin, J., & Hu, K. (2022) · Applied Sciences · doi:10.3390/app12125784 · Cited by 38
Metadata
- 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)
- Venue
- 2011 International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2011-11
- Pages
- 2556-2563
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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