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ECCV
2006
Springer

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

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Tracking Objects Across Cameras by Incrementally Learning Inter-camera Colour Calibration and Patterns of Activity
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.
Andrew Gilbert, Richard Bowden
Added 16 Oct 2009
Updated 16 Oct 2009
Type Conference
Year 2006
Where ECCV
Authors Andrew Gilbert, Richard Bowden
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