Multimodal information fusion and temporal integration for violence detection in movies

Abstract

This paper presents a violent shots detection system that studies several methods for introducing temporal and multimodal information in the framework. It also investigates different kinds of Bayesian network structure learning algorithms for modelling these problems. The system is trained and tested using the MediaEval 2011 Affect Task corpus, which comprises of 15 Hollywood movies. It is experimentally shown that both multimodality and temporality add interesting information into the system. Moreover, the analysis of the links between the variables of the resulting graphs yields important observations about the quality of the structure learning algorithms. Overall, our best system achieved 50% false alarms and 3% missed detection, which is among the best submissions in the MediaEval campaign.