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Open Access

Person Re-Identification with Effectively Designed Parts

Tsinghua University, Beijing 100084, China.
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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

person re-IDentification (re-ID)Convolutional Neural Network (CNN)part model

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Tsinghua Science and Technology
Volume 25 Issue 3,
June 2020
Pages 415-424
DOI: 10.26599/TST.2019.9010031
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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Received: 28 December 2018
Revised: 22 June 2019
Accepted: 17 July 2019
Published: 07 October 2019
© The author(s) 2020

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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