HMDB: A Large Video Database for Human Motion Recognition
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Poggio-HMDB.pdf
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Author(s)
Kuehne, H.
Serre, T.
Jhuang, H.
Garrote, Estibaliz
Poggio, Tomaso A.
Date Issued
January 2012
Journal
2011 IEEE International Conference on Computer Vision
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Kuehne, H. et al. “HMDB: A Large Video Database for Human Motion Recognition.” IEEE, 2011. 2556–2563. Web. 11 Apr. 2012. © 2012 Institute of Electrical and Electronics Engineers
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICCV.2011.6126543
