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Scalable Person Re-identification: A Benchmark

Zheng, Liang, Shen, Liyue, Tian, Lu, Wang, Shengjin, Wang, Jingdong, Tian, Qi·2015·2015 IEEE International Conference on Computer Vision (ICCV)

Abstract

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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Metadata

Authors
Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., & Tian, Q.
Venue
2015 IEEE International Conference on Computer Vision (ICCV)
Publisher
IEEE
Published
2015-12
Pages
1116-1124
OpenAlex
W2204750386
Cited by
4,506
References
5
FWCI
60.69
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Scalable Person Re-identification: A Benchmark. https://app.cochat.ai/papers/10.1109/iccv.2015.133
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Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., & Tian, Q. (2015). Scalable Person Re-identification: A Benchmark. 2015 IEEE International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2015.133

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