Post-Ranking Person Re- Identification using Discriminant Context Information Analysis


Authors : A.R.Atlin Beulah, Sworna Kokila.M.L

Volume/Issue : Volume 3 - 2018, Issue 1 - January

Google Scholar : https://goo.gl/DF9R4u

Scribd : https://goo.gl/W9zXKn

Thomson Reuters ResearcherID : https://goo.gl/3bkzwv

Person re-identification has become increasingly popular in the community due to its application and research significance. It aims at spotting a person of interest in other cameras.The discriminant information analysis which transforms the original feature vectors by removing the common information, thus defining the discriminant feature. A novel post-ranking framework for person re-identification (an unsupervised post-ranking framework) is proposed toimprove the first ranking results and outperforms the state-of-the-art approaches. The analysis of the similar appearances of the first ranks can be helpful in detecting, hence removing, and such visual ambiguities. Once the initial ranking is available, content and context sets are extracted. Then, these are exploited to remove the visual ambiguities and to obtain the discriminant feature space which is finally exploited to compute the new ranking. We demonstrate on two pedestrian benchmarks that by learning a more discriminative representation, our method significantly improves the first ranks results.

Keywords : Person Re-Identification, Discriminant, Extracted.

CALL FOR PAPERS


Paper Submission Last Date
30 - April - 2021

Paper Review Notification
In 1-2 Days

Paper Publishing
In 2-3 Days

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.
Subscribe
OR

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.
Subscribe