Abstract

We propose an approach to detect violence in movies at video shot level using low-level and mid-level features. We use audio energy, pitch and Mel-Frequency Cepstral Coefficients (MFCC) features to represent the affective audio content of movies. For the affective visual content, we extract average motion information. To learn a model for violence detection, we choose a discriminative classification approach and use a two-class support vector machine (SVM). Within this task, we investigate whether affect-related features provide good representation of violence and also whether making abstractions from low-level features are better than directly using low-level data for the task.

@inproceedings{acar2012mediaeval,
  title={DAI Lab at MediaEval 2012 Affect Task: The Detection of Violent Scenes using Affective Features.},
  author={Acar, Esra and Albayrak, Sahin},
  booktitle={MediaEval},
  issn={1613-0073},
  urn={nbn:de:0074-927-7},
  year={2012}
}