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

Zheng, Feng and Shao, Ling (2016) Learning Cross-View Binary Identities for Fast Person Re-Identification. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16). UNSPECIFIED, USA.

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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.

Item Type: Book Section
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: Pure Connector
Date Deposited: 07 Feb 2017 02:42
Last Modified: 21 Oct 2022 09:30
URI: https://ueaeprints.uea.ac.uk/id/eprint/62339

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