Scalable person re-identification: A benchmark

Zheng, L; Shen, L; Tian, L; Wang, S; Wang, J; Tian, Q

Scalable person re-identification: A benchmark

Zheng, L

Shen, L Tian, L Wang, S Wang, J Tian, Q

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the IEEE International Conference on Computer Vision, 2015, 2015 International Conference on Computer Vision, ICCV 2015 pp. 1116 - 1124
Issue Date:: 2015-02-17

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Field	Value	Language
dc.contributor.author	Zheng, L https://orcid.org/0000-0002-1464-9500	en_US
dc.contributor.author	Shen, L	en_US
dc.contributor.author	Tian, L	en_US
dc.contributor.author	Wang, S	en_US
dc.contributor.author	Wang, J	en_US
dc.contributor.author	Tian, Q	en_US
dc.date.issued	2015-02-17	en_US
dc.identifier.citation	Proceedings of the IEEE International Conference on Computer Vision, 2015, 2015 International Conference on Computer Vision, ICCV 2015 pp. 1116 - 1124	en_US
dc.identifier.isbn	9781467383912	en_US
dc.identifier.issn	1550-5499	en_US
dc.identifier.uri	http://hdl.handle.net/10453/118027
dc.description.abstract	© 2015 IEEE. This paper contributes a new high quality dataset for person re-identification, named "Market-1501". Generally, current datasets: 1) are limited in scale, 2) consist of hand-drawn bboxes, which are unavailable under realistic settings, 3) have only one ground truth and one query image for each identity (close environment). To tackle these problems, the proposed Market-1501 dataset is featured in three aspects. First, it contains over 32,000 annotated bboxes, plus a distractor set of over 500K images, making it the largest person re-id dataset to date. Second, images in Market-1501 dataset are produced using the Deformable Part Model (DPM) as pedestrian detector. Third, our dataset is collected in an open system, where each identity has multiple images under each camera. As a minor contribution, inspired by recent advances in large-scale image search, this paper proposes an unsupervised Bag-of-Words descriptor. We view person re-identification as a special task of image search. In experiment, we show that the proposed descriptor yields competitive accuracy on VIPeR, CUHK03, and Market-1501 datasets, and is scalable on the large-scale 500k dataset.	en_US
dc.relation.ispartof	Proceedings of the IEEE International Conference on Computer Vision	en_US
dc.relation.isbasedon	10.1109/ICCV.2015.133	en_US
dc.title	Scalable person re-identification: A benchmark	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2015 International Conference on Computer Vision, ICCV 2015	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	2015 International Conference on Computer Vision, ICCV 2015	en_US

Abstract:

© 2015 IEEE. This paper contributes a new high quality dataset for person re-identification, named "Market-1501". Generally, current datasets: 1) are limited in scale, 2) consist of hand-drawn bboxes, which are unavailable under realistic settings, 3) have only one ground truth and one query image for each identity (close environment). To tackle these problems, the proposed Market-1501 dataset is featured in three aspects. First, it contains over 32,000 annotated bboxes, plus a distractor set of over 500K images, making it the largest person re-id dataset to date. Second, images in Market-1501 dataset are produced using the Deformable Part Model (DPM) as pedestrian detector. Third, our dataset is collected in an open system, where each identity has multiple images under each camera. As a minor contribution, inspired by recent advances in large-scale image search, this paper proposes an unsupervised Bag-of-Words descriptor. We view person re-identification as a special task of image search. In experiment, we show that the proposed descriptor yields competitive accuracy on VIPeR, CUHK03, and Market-1501 datasets, and is scalable on the large-scale 500k dataset.

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http://hdl.handle.net/10453/118027