Material Detail

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 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.

This will delete the comment from the database. This operation is not reversible. Are you sure you want to do it?

Thank you for reporting a broken "Go to Material" link in MERLOT to help us maintain a collection of valuable learning materials.

Would you like to be notified when it's fixed?

Your email address

Do you know the correct URL for the link?

Suggested URL

Your broken link report has been sent to the MERLOT Team. Thank you for helping MERLOT maintain a current collection of valuable learning materials!

Your broken link report failed to be sent. Please try reloading the page and reporting it again. Thank you! Sorry for the trouble.

If you feel this material is inappropriate for the MERLOT Collection, please click SEND REPORT, and the MERLOT Team will investigate. Thank you!

Your inappropriate material report has been sent to the MERLOT Team. Thank you for helping MERLOT maintain a valuable collection of learning materials.

Your inappropriate material report failed to be sent. Please try reloading the page and reporting it again. Thank you! Sorry for the trouble.

Your inappropriate comment report has been sent to the MERLOT Team. Thank you for helping MERLOT maintain a valuable collection of learning materials.

You are being taken to the material on another site. This will open a new window.

You just viewed Transitive Re-identification. Please take a moment to rate this material.