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 scal- able static image datasets containing thousands of image categories, human action datasets lag far behind. Cur- rent action recognition databases contain on the order of ten different action categories collected under fairly con- trolled 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 is- sue we collected the largest action video database to-date with 51 action categories, which.
