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07 October 2019
Person Re-Identification with Effectively Designed Parts
Yali Li(
),
Shengjin Wang(
)
),
Shengjin Wang(
)
Cite this article:
Zhao Y, Li Y, Wang S.
Person Re-Identification with Effectively Designed Parts.
Tsinghua Science and Technology,
2020, 25(3): 415-424.
https://doi.org/10.26599/TST.2019.9010031
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Person re-IDentification (re-ID) is an important research topic in the computer vision community, with significance for a range of applications. Pedestrians are well-structured objects that can be partitioned, although detection errors cause slightly misaligned bounding boxes, which lead to mismatches. In this paper, we study the person re-identification performance of using variously designed pedestrian parts instead of the horizontal partitioning routine typically applied in previous hand-crafted part works, and thereby obtain more effective feature descriptors. Specifically, we benchmark the accuracy of individual part matching with discriminatively trained Convolutional Neural Network (CNN) descriptors on the Market-1501 dataset. We also investigate the complementarity among different parts using combination and ablation studies, and provide novel insights into this issue. Compared with the state-of-the-art, our method yields a competitive accuracy rate when the best part combination is used on two large-scale datasets (Market-1501 and CUHK03) and one small-scale dataset (VIPeR).
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Person Re-Identification with Effectively Designed Parts
Yali Li(
), Shengjin Wang(
)
), Shengjin Wang(
)
Abstract
Person re-IDentification (re-ID) is an important research topic in the computer vision community, with significance for a range of applications. Pedestrians are well-structured objects that can be partitioned, although detection errors cause slightly misaligned bounding boxes, which lead to mismatches. In this paper, we study the person re-identification performance of using variously designed pedestrian parts instead of the horizontal partitioning routine typically applied in previous hand-crafted part works, and thereby obtain more effective feature descriptors. Specifically, we benchmark the accuracy of individual part matching with discriminatively trained Convolutional Neural Network (CNN) descriptors on the Market-1501 dataset. We also investigate the complementarity among different parts using combination and ablation studies, and provide novel insights into this issue. Compared with the state-of-the-art, our method yields a competitive accuracy rate when the best part combination is used on two large-scale datasets (Market-1501 and CUHK03) and one small-scale dataset (VIPeR).
Keywords:
Convolutional Neural Network (CNN), person re-IDentification (re-ID), part model
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Publication history
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Acknowledgements
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Publication history
Received: 28 December 2018
Revised: 22 June 2019
Accepted: 17 July 2019
Published:
07 October 2019
Issue date: June 2020
Copyright
© The author(s) 2020
Acknowledgements
This work was supported by the National Natural Science Foundation of China (Nos. 61771288 and 61701277) and the State Key Development Program of the 13th Five-Year Plan (No. 2017YFC0821601).
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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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