We address recognition and localization of human actions in realistic scenarios. In contrast to the previous work studying human actions in controlled settings, here we train and test algorithms on real movies with substantial variation of actions in terms of subject appearance, motion, surrounding scenes, viewing angles and spatio-temporal extents. We introduce a new annotated human action dataset and use it to evaluate several existing methods. We in particular focus on boosted space-time window classifiers and introduce "keyframe priming" that combines discriminative models of human motion and shape within an action. Keyframe priming is shown to significantly improve the performance of action detection. We present detection results for the action class "drinking" evaluated on two episodes of the movie "Coffee and Cigarettes".
Laptev et al. REF pioneered one of the first works on action detection in realistic scenarios, with a boosted 3D space-time window classifier combined with a single frame action detector.
Retrieving actions in movies
2007 IEEE 11th International Conference on Computer Vision
2007 IEEE 11th International Conference on Computer Vision
Computer Science