|Description (include details on usage, files and paper references)||
Hollywood-2 datset contains 12 classes of human actions and 10 classes of scenes distributed over 3669 video clips and approximately 20.1 hours of video in total. The dataset intends to provide a comprehensive benchmark for human action recognition in realistic and challenging settings. The dataset is composed of video clips extracted from 69 movies, it contains approximately 150 samples per action class and 130 samples per scene class in training and test subsets. A part of this dataset was originally used in the paper "Actions in Context", Marszałek et al. in Proc. CVPR 2009. Hollywood-2 is an extension of the earlier Hollywood dataset.
Hollywood Human Actions dataset (CVPR 2008)
Hollywood dataset contains video samples with human action from 32 movies. Each sample is labeled according to one or more of 8 action classes: AnswerPhone, GetOutCar, HandShake, HugPerson, Kiss, SitDown, SitUp, StandUp. The dataset is divided into a test set obtained from 20 movies and two training sets obtained from 12 movies different from the test set. The Automatic training set is obtained using automatic script-based action annotation and contains 233 video samples with approximately 60% correct labels. The Clean training set contains 219 video samples with manually verified labels. The test set contains 211 samples with manually verified labels. More details on the dataset can be obtained here. The dataset was originally used in the paper "Learning Realistic Human Actions from Movies", Ivan Laptev, Marcin Marszałek, Cordelia Schmid and Benjamin Rozenfeld; in Proc. CVPR 2008. See on-line paper description here.