Bridging the gaps between cameras

  • Authors:
  • Dimitrios Makris;Tim Ellis;James Black

  • Affiliations:
  • Digital Imaging Research Centre, Kingston University, Kingston-upon-Thames, Surrey, UK;Digital Imaging Research Centre, Kingston University, Kingston-upon-Thames, Surrey, UK;Digital Imaging Research Centre, Kingston University, Kingston-upon-Thames, Surrey, UK

  • Venue:
  • CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
  • Year:
  • 2004

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Abstract

The paper investigates the unsupervised learning of a model of activity for a multi-camera surveillance network that can be created from a large set of observations. This enables the learning algorithm to establish links between camera views associated with an activity. The learning algorithm operates in a correspondence-free manner, exploiting the statistical consistency of the observation data. The derived model is used to automatically determine the topography of a network of cameras and to provide a means for tracking targets across the "blind" areas of the network. A theoretical justification and experimental validation of the methods are provided.