TAILIEUCHUNG - HMDB: A Large Video Database for Human Motion Recognition

With Oracle Support, you know up front and with certainty how long your Oracle products are supported. The Lifetime Support Policy provides access to technical experts for as long as you license your Oracle products and consists of three support stages: Premier Support, Extended Support, and Sustaining Support. It delivers maximum value by providing you with rights to major product releases so you can take full advantage of technology and product enhancements. Your technology and your business keep moving forward together. Premier Support provides a standard five-year support policy for Oracle Technology products. You can extend support for an additional. | HMDB A Large Video Database for Human Motion Recognition H. Kuehne Karlsruhe Instit. of Tech. Karlsruhe Germany kuehne@ H. Jhuang E. Garrote T. Poggio Massachusetts Institute of Technology Cambridge MA 02139 hueihan@ tp@ T. Serre Brown University Providence RI 02906 thomas_serre@ 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. 1. Introduction With several billion videos currently available on the internet and approximately 24 hours of video uploaded to YouTube every minute there is an immediate need for robust algorithms that can help organize summarize and retrieve this massive amount of data. While much effort has been devoted to the collection of realistic internetscale static image databases 17 23 27 4 5 current action recognition datasets lag far behind. The most popular benchmark datasets such as KTH 20 Weizmann 3 or the IXMAS dataset 25 contain around 6-11 actions each. A typical video .

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