Information Technology Reference
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Chapter 11
Detecting Violent Content in Hollywood
Movies and User-Generated Videos
Esra Acar, Melanie Irrgang, Dominique Maniry and Frank Hopfgartner
Abstract
Detecting violent scenes in videos is an important content understanding
functionality, e.g., for providing automated youth protection services. The key issues
in designing violence detection algorithms are the choice of discriminative features
and learning effective models. We employ low and mid-level audio-visual features
and evaluate their discriminative power within the context of the MediaEval Violent
Scenes Detection (VSD) task. The audio-visual cues are fused at the decision level.
As audio features, Mel-Frequency Cepstral Coefficients (MFCC), and as visual fea-
tures dense histogram of oriented gradient (HoG), histogram of oriented optical flow
(HoF), Violent Flows (ViF), and affect-related color descriptors are used. We perform
feature space partitioning of the violence training samples through
k
-means clustering
and train a different model for each cluster. These models are then used to predict the
violence level of videos by employing two-class support vector machines (SVMs).
The experimental results in Hollywood movies and short web videos show that
mid-level audio features are more discriminative than the visual features, and that the
performance is further enhanced by fusing the audio-visual cues at the decision level.
Babysitting
Nowadays our children are submerged by connected equipment, whether this is at
school, at home, or even in the car. Notable examples of such equipment include TV,
cable or satellite set-top boxes, tablets or the smartphones of the parents, when those
let their children play with it.
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