article

Editor's Choice Article: A survey of approaches and trends in person re-identification

Authors:

Apurva Bedagkar-Gala,

Shishir K. ShahAuthors Info & Claims

Image and Vision Computing, Volume 32, Issue 4

Pages 270 - 286

https://doi.org/10.1016/j.imavis.2014.02.001

Published: 01 April 2014 Publication History

Abstract

Person re-identification is a fundamental task in automated video surveillance and has been an area of intense research in the past few years. Given an image/video of a person taken from one camera, re-identification is the process of identifying the person from images/videos taken from a different camera. Re-identification is indispensable in establishing consistent labeling across multiple cameras or even within the same camera to re-establish disconnected or lost tracks. Apart from surveillance it has applications in robotics, multimedia and forensics. Person re-identification is a difficult problem because of the visual ambiguity and spatiotemporal uncertainty in a person's appearance across different cameras. These difficulties are often compounded by low resolution images or poor quality video feeds with large amounts of unrelated information in them that does not aid re-identification. The spatial or temporal conditions to constrain the problem are hard to capture. However, the problem has received significant attention from the computer vision research community due to its wide applicability and utility. In this paper, we explore the problem of person re-identification and discuss the current solutions. Open issues and challenges of the problem are highlighted with a discussion on potential directions for further research.

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Editor's Choice Article: A survey of approaches and trends in person re-identification
1. Computing methodologies
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    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks

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cover image Image and Vision Computing

Image and Vision Computing Volume 32, Issue 4

April, 2014

76 pages

ISSN:0262-8856

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Copyright © Elsevier B.V. © 2014.

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Butterworth-Heinemann

United States

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Published: 01 April 2014

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Cited By

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https://dl.acm.org/doi/10.1109/TCSVT.2023.3287300
Gao HHu CHan GMao JHuang WGuan Q(2024)Point-level feature learning based on vision transformer for occluded person re-identificationImage and Vision Computing10.1016/j.imavis.2024.104929143:COnline publication date: 1-Mar-2024
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Chouchane ABessaoudi MKheddar HOuamane AVieira THassaballah M(2024)Multilinear subspace learning for Person Re-Identification based fusion of high order tensor featuresEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107521128:COnline publication date: 14-Mar-2024
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Qian YBarthelemy JKaruppiah EPerez P(2024)Identifying Re-identification Challenges: Past, Current and Future TrendsSN Computer Science10.1007/s42979-024-03271-95:7Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1007/s42979-024-03271-9
Poongothai EDeepthi KJahnavi Y(2024)Analysis of Pose Estimation Based GLOGT Feature Extraction for Person Re-Identification in Surveillance Area NetworkWireless Personal Communications: An International Journal10.1007/s11277-024-11489-2138:1(245-268)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s11277-024-11489-2
Wang BZhang MDing PYang TJin YXu Y(2024)User re-identification via human mobility trajectories with siamese transformer networksApplied Intelligence10.1007/s10489-023-05234-854:1(815-834)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s10489-023-05234-8
Yin KDing ZDong ZJi XWang ZChen DLi YYin GWang Z(2024)Person re-identification method based on fine-grained feature fusion and self-attention mechanismComputing10.1007/s00607-024-01270-5106:5(1681-1705)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s00607-024-01270-5
Wu ZHu ZDing J(2024)Same-clothes person re-identification with dual-stream networkMultimedia Systems10.1007/s00530-024-01269-030:2Online publication date: 26-Feb-2024
https://dl.acm.org/doi/10.1007/s00530-024-01269-0
Abed AAkrout BAmous I(2024)Deep learning-based few-shot person re-identification from top-view RGB and depth imagesNeural Computing and Applications10.1007/s00521-024-10239-636:31(19365-19382)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s00521-024-10239-6
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