Chapter

Fusion in Computer Vision

Part of the series Advances in Computer Vision and Pattern Recognition pp 161-184

Date:

Multimodal Fusion in Surveillance Applications

  • Virginia Fernandez ArguedasAffiliated withMultimedia and Vision Research Group, School of Electronic Engineering and Computer Science, Queen Mary, University of LondonEuropean Commission—Joint Research Centre (JRC) Email author 
  • , Qianni ZhangAffiliated withMultimedia and Vision Research Group, School of Electronic Engineering and Computer Science, Queen Mary, University of London
  • , Ebroul IzquierdoAffiliated withMultimedia and Vision Research Group, School of Electronic Engineering and Computer Science, Queen Mary, University of London

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Abstract

The recent outbreak of vandalism, accidents and criminal activities has increased general public’s awareness about safety and security, demanding improved security measures. Smart surveillance video systems have become an ubiquitous platform which monitors private and public environments, ensuring citizens well-being. Their universal deployment integrates diverse media and acquisition systems, generating daily an enormous amount of multimodal data. Nowadays, numerous surveillance applications exploit multiple types of data and features benefitting from their uncorrelated contributions. Hence, the analysis, standardisation and fusion of complex content, specially visual, have become a fundamental problem to enhance surveillance systems by increasing their accuracy, robustness and reliability. During this chapter, an exhaustive survey of the existing multimodal fusion techniques and their applications in surveillance is provided. Addressing some of the revealed challenges from the state of the art, this chapter focuses on the development of a multimodal fusion technique for automatic surveillance object classification. The proposed fusion technique exploits the benefits of a Bayesian inference scheme to enhance surveillance systems’ performance. The chapter ends with an evaluation of the proposed Bayesian-based multimodal object classifier against two state-of-the-art object classifiers to demonstrate the benefits of multimodal fusion in surveillance applications.