Vision Based Semantic Analysis of Surveillance Videos

  • Virginia Fernandez Arguedas
  • Qianni Zhang
  • Krishna Chandramouli
  • Ebroul Izquierdo
Part of the Studies in Computational Intelligence book series (SCI, volume 418)

Abstract

As recent research in automatic surveillance systems has attracted many cross-domain researchers, a large-number of algorithms have been proposed for automating surveillance systems. The objective of this chapter is twofold: First, we present an extensive survey of different techniques that have been proposed for surveillance systems categorised into motion analysis, visual feature extraction and indexing. Second, an integrated surveillance framework for unsupervised object indexing is developed to study and evaluate the performance of visual features. The study focuses on two characteristics highly related with human visual perception, colour and texture. The set of visual features under analysis comprises two categories, new leading visual features versus state-of-the-art MPEG-7 visual features. The evaluation of the framework is carried out with AVSS 2007 and CamVid 2008 datasets.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Virginia Fernandez Arguedas
    • 1
  • Qianni Zhang
    • 1
  • Krishna Chandramouli
    • 1
  • Ebroul Izquierdo
    • 1
  1. 1.Multimedia and Vision Research Group, School of Electronic Engineering and Computer ScienceQueen Mary, University of LondonLondonUK

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