ARF @ MediaEval 2012: An Uninformed Approach to Violence Detection in Hollywood Movies
نویسندگان
چکیده
The MediaEval 2012 Affect Task challenged participants to automatically find violent scenes in a set of Hollywood movies. We propose to first predict a set of mid-level concept annotations from low-level visual and auditory features, then fuse the concept predictions and features to detect violent content. Instead of engineering features suitable for the task, we deliberately restrict ourselves to simple generalpurpose features with limited temporal context and a generic neural network classifier, setting a baseline for more sophisticated approaches. On 3 test movies, our system detects 49% of violent frames at a precision of 28%, outperforming all other submissions.
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