Authors:
Daniel Helm
;
Florian Kleber
and
Martin Kampel
Affiliation:
Computer Vision Lab, Institute of Visual Computing and Human-Centered Technology, TU Wien, Favoritenstraße 9/193-1, Vienna, Austria
Keyword(s):
Historical Film Preservation, Film Archives, Deep Learning, Automated Film Analysis, Film Shot Dataset.
Abstract:
Automated shot type classification plays a significant role in film preservation and indexing of film datasets. In this paper a historical shot type dataset (HistShot) is presented, where the frames have been extracted from original historical documentary films. A center frame of each shot has been chosen for the dataset and is annotated according to the following shot types: Close-Up (CU), Medium-Shot (MS), Long-Shot (LS), Extreme-Long-Shot (ELS), Intertitle (I), and Not Available/None (NA). The validity to choose the center frame is shown by a user study. Additionally, standard CNN-based methods (ResNet50, VGG16) have been applied to provide a baseline for the HistShot dataset.