HMDB51: A Large Video Database for Human Motion Recognition

Hilde Kuehne (), Hueihan Jhuang (), Rainer Stiefelhagen () and Thomas Serre ()
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Hilde Kuehne: KIT, Institue for Anthorpomathics
Hueihan Jhuang: Max Planck Institute for Intelligent Systems, Perceiving Systems Department
Rainer Stiefelhagen: KIT, Institue for Anthorpomathics
Thomas Serre: Brown University, Institute for Brain Sciences

A chapter in High Performance Computing in Science and Engineering ‘12, 2013, pp 571-582 from Springer

Abstract: 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.

Keywords: Action Recognition Database; Camera Motion; Activity Recognition; Human Motion Database (HMDB); Visible Body Parts (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/978-3-642-33374-3_41

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