UCF50: Action Recognition in Realistic Videos

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UCF50: Action Recognition in Realistic Videos

UCF50 is an action recognition data set with 50 action categories, consisting of realistic videos taken from youtube. This data set is an extension of Youtube Action data set (UCF11) which has 11 action categories. Most of the available action recognition data sets are not realistic and are staged by actors. In this data set, the primary focus is to provide the computer vision community with an action recognition data set consisting of realistic videos which are taken from Youtube. The data set is very challenging due to large variations in camera motion, object appearance and pose, object scale, viewpoint, cluttered background, illumination conditions, etc. For all the 50 categories, the videos are grouped into 25 groups, where each group consists of more than 4 action clips. The video clips in the same group may share some common features, such as the same person, similar background, similar viewpoint, and so on.

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