Incremental Modelling of the Posterior Distribution of Objects for Inter and Intra Camera Tracking

A. Gilbert, R. Bowden
<span title="">2005</span> <i title="British Machine Vision Association"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/6bfo5625nvdfvbgyf7ldi5wmfe" style="color: black;">Procedings of the British Machine Vision Conference 2005</a> </i> &nbsp;
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 create the spatio-temporal links between cameras, and thus model the posterior probability distribution of these links. This can then be used with an appearance model of the object to track across cameras. It requires no calibration or batch preprocessing and becomes more accurate over time as evidence is accumulated.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5244/c.19.39">doi:10.5244/c.19.39</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/bmvc/GilbertB05.html">dblp:conf/bmvc/GilbertB05</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xn3qefrwk5eebjt6jmfith5ucy">fatcat:xn3qefrwk5eebjt6jmfith5ucy</a> </span>
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