Audio and Visual Processing for Violence Detection in Movies
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For detecting violent scenes in movies, we employed fusion methodologies, based on learning, knowledge representation and reasoning. Towards this goal, a multi-step approach was followed: initially, automated audio and visual analysis was performed to extract audio and visual cues. Then, two different fusion approaches are deployed: i) a multimodal one that provides binary decisions on the existence of violence or not, employing machine learning techniques, ii) an ontological and reasoning one, that combines the audio-visual cues with violence and multimedia ontologies. The latter reasons out not only the existence of violence or not in a video scene, but also the type of violence (fight, screams, gunshots). Both approaches were experimentally tested, validated and compared for the binary decision problem of violence detection. Finally, results for the violence type identification are presented for the ontological fusion approach. For evaluation purposes, a large dataset of real movie data has been populated.
