Tracking objects across cameras by incrementally learning inter-camera colour calibration and patterns of activity
Gilbert, A and Bowden, R (2006) Tracking objects across cameras by incrementally learning inter-camera colour calibration and patterns of activity In: ECCV 2006, 2006-05-07 - 2006-05-13, Graz, Austria.
GilbertBowdenECCV06.pdf - Accepted version Manuscript
Available under License : See the attached licence file.
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.
|Item Type:||Conference or Workshop Item (Conference Paper)|
|Divisions :||Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing|
|Identification Number :||https://doi.org/10.1007/11744047_10|
|Uncontrolled Keywords :||VISUAL SURVEILLANCE|
|Additional Information :||The original publication is available at: http://www.springerlink.com|
|Depositing User :||Symplectic Elements|
|Date Deposited :||11 Jun 2012 14:07|
|Last Modified :||23 Sep 2013 19:24|
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