Transitive re-identification

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 significantly reduces the annotation effort inherent in a learning system, which goes down from O(N2) to O(N), for a site containing N cameras. Moreover, when adding camera (N +1), only one inter-camera training set is required for establishing all correspondences. In our experiments we found that the method is effective and more accurate than the competing camera invariant approach.