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. International Joint Conferences on Artificial Intelligence, pp. 2399-2406. ISBN 978-1-57735-771-1

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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.

Item Type:	Book Section
Subjects:	G700 Artificial Intelligence
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Becky Skoyles
Date Deposited:	03 Jan 2017 14:47
Last Modified:	12 Oct 2019 22:27
URI:	http://nrl.northumbria.ac.uk/id/eprint/28996

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