Learning Cross-View Binary Identities for Fast Person Re-Identification

Feng Zheng, Ling Shao

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

39 Citations (Scopus)

Abstract

In this paper, we propose to learn cross-view binary identities (CBI) for fast person re-identification. To achieve this, two sets of discriminative hash functions for two different views are learned by simultaneously minimising their distance in the Hamming space, and maximising the cross-covariance and margin. Thus, similar binary codes can be found for images of a same person captured at different views by embedding the images into the Hamming space. Therefore, person re-identification can be solved by efficiently computing and ranking the Hamming distances between the images. Extensive experiments are conducted on two public datasets and CBI produces comparable results as state-ofthe- art re-identification approaches but is at least 2200 times faster.
Original languageEnglish
Title of host publicationProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence
PublisherInternational Joint Conferences on Artificial Intelligence
Pages2399-2406
ISBN (Print)978-1-57735-771-1
Publication statusPublished - 2016

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