Beat-Event Detection in Action Movie Franchises
This work addresses the problem of semantic video analysis for movie content creators and researchers, but it is incremental as it builds on existing action localization methods with a new dataset and constraints.
The paper tackles the challenge of detecting and classifying long video chunks into broad semantic categories like 'pursuit' or 'romance' in action movies, by introducing a new dataset and a method that uses shot classification and temporal constraints, showing that temporal constraints significantly improve classification performance.
While important advances were recently made towards temporally localizing and recognizing specific human actions or activities in videos, efficient detection and classification of long video chunks belonging to semantically defined categories such as "pursuit" or "romance" remains challenging.We introduce a new dataset, Action Movie Franchises, consisting of a collection of Hollywood action movie franchises. We define 11 non-exclusive semantic categories - called beat-categories - that are broad enough to cover most of the movie footage. The corresponding beat-events are annotated as groups of video shots, possibly overlapping.We propose an approach for localizing beat-events based on classifying shots into beat-categories and learning the temporal constraints between shots. We show that temporal constraints significantly improve the classification performance. We set up an evaluation protocol for beat-event localization as well as for shot classification, depending on whether movies from the same franchise are present or not in the training data.