Deep feature learning for person re-identification in a large-scale crowdsourced environment

Finding the same individual across cameras in disjoint views at different locations and times, which is known as person re-identification (re-id), is an important but difficult task in intelligent visual surveillance. However, to build a practical re-id system for large-scale and crowdsourced enviro...

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Bibliographic Details
Published inThe Journal of supercomputing Vol. 74; no. 12; pp. 6753 - 6765
Main Authors Oh, Seon Ho, Han, Seung-Wan, Choi, Bum-Suk, Kim, Geon-Woo, Lim, Kyung-Soo
Format Journal Article
LanguageEnglish
Published New York Springer US 01.12.2018
Springer Nature B.V
Subjects
Compilers
Complexity
Computer memory
Computer Science
Crowdsourcing
Interpreters
Processor Architectures
Programming Languages
State of the art
Visual tasks
Crowdsourcing
Deep feature learning
Visual surveillance
Large-scale environment
Person re-identification
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Summary:Finding the same individual across cameras in disjoint views at different locations and times, which is known as person re-identification (re-id), is an important but difficult task in intelligent visual surveillance. However, to build a practical re-id system for large-scale and crowdsourced environments, the existing approaches are largely unsuitable because of their high model complexity. In this paper, we present a deep feature learning framework for automated large-scale person re-id with low computational cost and memory usage. The experimental results show that the proposed framework is comparable to the state-of-the-art methods while having low model complexity.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-017-2221-5