Proceedings

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

Feng Zheng, Ling Shao

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Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 2399-2405.

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.

Feng Zheng, and Ling Shao. 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. IJCAI, 2399–2405. https://ijcai.org/proceedings/2016/342
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