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

Feng Zheng, Ling Shao

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

38 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 (IJCAI-16)
Publication statusPublished - 2016
Event25th International Joint Conference on Artificial Intelligence - New York, United States
Duration: 9 Jul 201615 Jul 2016
http://ijcai-16.org/index.php/welcome/view/home

Conference

Conference25th International Joint Conference on Artificial Intelligence
CountryUnited States
CityNew York
Period9/07/1615/07/16
Internet address

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