published: April 3, 2014, recorded: September 2013, views: 2174
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !