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Tracking Objects Across Cameras by Incrementally Learning Inter-camera Colour Calibration and Patterns of Activity

  • Andrew Gilbert
  • Richard Bowden
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3952)

Abstract

This paper presents a scalable solution to the problem of tracking objects across spatially separated, uncalibrated, non-overlapping cameras. Unlike other approaches this technique uses an incremental learning method, to model both the colour variations and posterior probability distributions of spatio-temporal links between cameras. These operate in parallel and are then used with an appearance model of the object to track across spatially separated cameras. The approach requires no pre-calibration or batch preprocessing, is completely unsupervised, and becomes more accurate over time as evidence is accumulated.

Keywords

Appearance Model Colour Similarity Observation Likelihood Coarse Quantisation Valid Link 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Andrew Gilbert
    • 1
  • Richard Bowden
    • 1
  1. 1.CVSSPUniversity of SurreyGuildfordEngland

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