Re-id: Hunting Attributes in the Wild

Ryan Layne, Timothy M. Hospedales, Shaogang Gong

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Person re-identification is a crucial capability underpinning many applications of public-space video surveillance. Recent studies have shown the value of learning semantic attributes as a discriminative representation for re-identification. However, existing attribute representations do not generalise across camera deployments. Thus, this strategy currently requires the prohibitive effort of annotating a vector of person attributes for each individual in a large training set -- for each given deployment/dataset. In this paper we take a different approach and automatically discover a semantic attribute ontology, and learn an effective associated representation by crawling large volumes of internet data. In addition to eliminating the necessity for per-dataset annotation, by training on a much larger and more diverse array of examples this representation is more view-invariant and generalisable than attributes trained at conventional small scales. We show that these automatically discovered attributes provide a valuable representation that significantly improves re-identification performance on a variety of challenging datasets.
Original languageEnglish
Title of host publicationBritish Machine Vision Conference, BMVC 2014, Nottingham, UK, September 1-5, 2014
PublisherBMVA Press
Number of pages12
Publication statusPublished - 2014


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