|M.Sc Student||Brand Yulia|
|Department||Department of Electrical Engineering||Supervisors||Professor Michael Lindenbaum|
|Dr. Tamar Avraham|
|Full Thesis text|
Person re-identification accuracy can be substantially 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 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, from O(N2) to O(N), for a site containing N cameras. Moreover, when adding camera (N), 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.