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Transitive Re-identification

Transitive Re-identification

This video was recorded at British Machine Vision Conference (BMVC), Bristol 2013. Person re-identification accuracy can be significantly improved given a training set that demonstrates changes in appearances associated with the two non-overlapping cameras involved. Here we test whether this advantage can be maintained when directly annotated training sets are not available for all camera-pairs at the site. Given the training sets capturing correspondences between cameras A and B and a different training set capturing correspondences between cameras B and C, the Transitive Re-IDentification algorithm (TRID) suggested here provides a classifier for (A;C) appearance pairs. The proposed method is based on statistical modeling and uses a marginalization process for the inference. This approach... Show More

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