HMDB:用于人体动作识别的大型视频数据库

Hilde Kuehne, Hueihan Jhuang, Estíbaliz Garrote, T. Poggio, Thomas Serre
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引用次数: 3246

摘要

每天有近10亿在线视频被观看,计算机视觉研究的一个新兴前沿是视频的识别和搜索。虽然在包含数千个图像类别的大型可扩展静态图像数据集的收集和注释方面已经投入了大量的努力,但人类动作数据集远远落后。当前的动作识别数据库包含在相当控制的条件下收集的大约十种不同的动作类别。这些数据集的最先进性能现在接近上限,因此需要设计和创建新的基准。为了解决这个问题,我们收集了迄今为止最大的动作视频数据库,其中包含51个动作类别,总共包含大约7,000个手动注释的剪辑,这些剪辑来自各种来源,从数字化电影到YouTube。我们使用该数据库评估了两个具有代表性的计算机视觉系统在动作识别方面的性能,并探讨了这些方法在摄像机运动、视点、视频质量和遮挡等各种条件下的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
HMDB: A large video database for human motion recognition
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.
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