The Action Movie Franchises dataset consists of 20 Hollywood action movies belonging to 5 famous franchises: Rambo, Rocky, Die Hard, Lethal Weapon, Indiana Jones. Each franchise comprises 4 movies.
Each movie is decomposed into a list of shots, extracted with a shot boundary detector. Each shot is tagged with zero, one or several labels corresponding to the 11 beat-categories (the label NULL is assigned to shots with zero labels):
The set of categories was inspired by the taxonomy of [Snyder, 2005], and motivated by the presence of common narrative structures and beats in action movies. Indeed, category definitions strongly rely on a split of the characters into "good" and "bad" tags, which is typical in such movies. Each category thus involves a fixed combination of heroes and villains: both "good" and "bad" characters are present during battle and pursuit, but only "good" heroes are present in the case of good argue good.
Large intra-class variation is due to a number of factors: duration, intensity of action, objects and actors, and different scene locations, camera viewpoint, filming style. For ambiguous cases we used the "difficult" tag.
Each movie is first temporally segmented into a sequence of shots using a shot boundary detector. Shot boundaries correspond to transitions between different cameras and/or scene locations. The minimal temporal unit in the annotation process is a shot, so temporal boundaries of beat-events always coincide with shot boundaries. Shots can be annotated with zero, one, or several category labels. Shots without an annotation are assigned a NULL label. All occurrences are annotated, so NULL shots are negative instances for each of the 11 beat-event categories.
The annotation process was carried out in two passes. First, we have manually annotated each shot with zero, one or several of the 11 beat-category labels. Then, we have annotated the beat-events by specifying their category, beginning and ending shots. We tolerated gaps of 1-2 unrelated shots for sufficiently consistent beat-events. Indeed, movies are often edited into sequences of interleaved shots from two events, e.g. between the main storyline and the "B" story.[Video examples of beat-events]
|Duration (h=hour, s=second)|
|annotation unit||# positive train units||average unit||annotation||NULL||coverage|
Legend: green - correct detection, red - false alarm, gray - ignored, bottom line - ground truth.
Shot categories are shown in different colors, hashes show difficult cases.