Bayesian multimodal fusion in forensic applications

Authors:
Virginia Fernandez Arguedas;Qianni Zhang;Ebroul Izquierdo
Affiliations:
Multimedia and Vision Research Group, School of Electronic Engineering and Computer Science, Queen Mary, University of London, London, UK;Multimedia and Vision Research Group, School of Electronic Engineering and Computer Science, Queen Mary, University of London, London, UK;Multimedia and Vision Research Group, School of Electronic Engineering and Computer Science, Queen Mary, University of London, London, UK
Venue:
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Year:
2012

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Abstract

The public location of CCTV cameras and their connexion with public safety demand high robustness and reliability from surveillance systems. This paper focuses on the development of a multimodal fusion technique which exploits the benefits of a Bayesian inference scheme to enhance surveillance systems' reliability. Additionally, an automatic object classifier is proposed based on the multimodal fusion technique, addressing semantic indexing and classification for forensic applications. The proposed Bayesian-based Multimodal Fusion technique, and particularly, the proposed object classifier are evaluated against two state-of-the-art automatic object classifiers on the i-LIDS surveillance dataset.