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. The goal of this effort is to provide a tool to evaluate the performance of 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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Acknowledgements
This report describes research done in part at the Center for Biological & Computational Learning, affiliated with MIBR, BCS, CSAIL at MIT. This research was sponsored by grants from DARPA (IPTO and DSO), NSF (NSF-0640097, NSF-0827427), AFSOR-THRL (FA8650-05- C-7262). Additional support was provided by: Adobe, King Abdullah University Science and Technology grant to B. DeVore, NEC, Sony and by the Eugene McDermott Foundation. This work is also done and supported by Brown University, Center for Computation and Visualization, and the Robert J. and Nancy D. Carney Fund for Scientific Innovation, by DARPA (DARPA-BAA-09-31), and ONR (ONR-BAA-11-001). H.K. was supported by a grant from the Ministry of Science, Research and the Arts of Baden Württemberg, Germany.
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Kuehne, H., Jhuang, H., Stiefelhagen, R., Serre, T. (2013). HMDB51: A Large Video Database for Human Motion Recognition. In: Nagel, W., Kröner, D., Resch, M. (eds) High Performance Computing in Science and Engineering ‘12. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33374-3_41
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DOI: https://doi.org/10.1007/978-3-642-33374-3_41
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