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Person Re-Identification Based on Computer Vision
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Person Re-Identification Based on Computer Vision

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (20 February 2024) | Viewed by 13391

Special Issue Editors

Prof. Dr. Jie Qin

E-Mail Website
Guest Editor
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Interests: computer vision; machine learning; pattern recognition; image/video processing; human activity analysis; person re-identification; content-based image/video retrieval; zero-shot learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mang Ye

E-Mail Website
Guest Editor
School of Computer Science, Wuhan University, Wuhan 430072, China
Interests: computer vision; object tracking; person re-identification; unsupervised learning
Dr. Yichao Yan

E-Mail Website
Guest Editor
AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: neural rendering; domain adaptation/generalization; person re-identification; image/video generation; action recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Jiaxin Chen

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Beihang University, Beijing 100191, China
Interests: computer vision; machine learning

Special Issue Information

Dear Colleagues,

Person re-identification (re-id) is an important research topic in computer vision and has received tremendous research efforts in the past decade. Existing works have addressed the re-id problem from various perspectives. However, more efforts are still needed to make the re-id approaches more reliable, robust, efficient, and generalizable. Therefore, this Special Issue aims to solicit original research from both the industry and academia on recent advances, solutions, applications and new challenges in the field of person re-identification based on computer vision techniques. The topics of interest include (but are not limited to) the following areas:

  • Challenges in person re-id and related tasks;
  • Robust person re-id systems;
  • Domain adaptation/generalization for person re-id;
  • Cross-modal or multi-modal person re-id;
  • Joint person detection and re-id;
  • Partial/occluded person re-id;
  • Video-based person re-id;
  • Lightweight or efficient models for person re-id;
  • Multi-sensor feature fusion for person re-id;
  • Novel benchmarks for person re-id;
  • Surveys/reviews for person re-id and related tasks

Prof. Dr. Jie Qin
Prof. Dr. Mang Ye
Dr. Yichao Yan
Dr. Jiaxin Chen
Guest Editors

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • person re-identification
  • pedestrian detection
  • person search
  • image retrieval
  • cross-modal retrieval
  • multi-modal fusion
  • domain adaptation
  • domain generalization
  • transfer learning

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Published Papers (5 papers)

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Research

17 pages, 3630 KiB  
Article
A Generative Approach to Person Reidentification
by Andrea Asperti, Salvatore Fiorilla and Lorenzo Orsini
Sensors 2024, 24(4), 1240; https://doi.org/10.3390/s24041240 - 15 Feb 2024
Cited by 5 | Viewed by 2121
Abstract
Person Re-identification is the task of recognizing comparable subjects across a network of nonoverlapping cameras. This is typically achieved by extracting from the source image a vector of characteristic features of the specific person captured by the camera. Learning a good set of [...] Read more.
Person Re-identification is the task of recognizing comparable subjects across a network of nonoverlapping cameras. This is typically achieved by extracting from the source image a vector of characteristic features of the specific person captured by the camera. Learning a good set of robust, invariant and discriminative features is a complex task, often leveraging contrastive learning. In this article, we explore a different approach, learning the representation of an individual as the conditioning information required to generate images of the specific person starting from random noise. In this way we decouple the identity of the individual from any other information relative to a specific instance (pose, background, etc.), allowing interesting transformations from one identity to another. As generative models, we use the recent diffusion models that have already proven their sensibility to conditioning in many different contexts. The results presented in this article serve as a proof-of-concept. While our current performance on common benchmarks is lower than state-of-the-art techniques, the approach is intriguing and rich of innovative insights, suggesting a wide range of potential improvements along various lines of investigation. Full article
(This article belongs to the Special Issue Person Re-Identification Based on Computer Vision)
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20 pages, 6296 KiB  
Article
Approaches to Improve the Quality of Person Re-Identification for Practical Use
by Timur Mamedov, Denis Kuplyakov and Anton Konushin
Sensors 2023, 23(17), 7382; https://doi.org/10.3390/s23177382 - 24 Aug 2023
Cited by 3 | Viewed by 2286
Abstract
The idea of the person re-identification (Re-ID) task is to find the person depicted in the query image among other images obtained from different cameras. Algorithms solving this task have important practical applications, such as illegal action prevention and searching for missing persons [...] Read more.
The idea of the person re-identification (Re-ID) task is to find the person depicted in the query image among other images obtained from different cameras. Algorithms solving this task have important practical applications, such as illegal action prevention and searching for missing persons through a smart city’s video surveillance. In most of the papers devoted to the problem under consideration, the authors propose complex algorithms to achieve a better quality of person Re-ID. Some of these methods cannot be used in practice due to technical limitations. In this paper, we propose several approaches that can be used in almost all popular modern re-identification algorithms to improve the quality of the problem being solved and do not practically increase the computational complexity of algorithms. In real-world data, bad images can be fed into the input of the Re-ID algorithm; therefore, the new Filter Module is proposed in this paper, designed to pre-filter input data before feeding the data to the main re-identification algorithm. The Filter Module improves the quality of the baseline by 2.6% according to the Rank1 metric and 3.4% according to the mAP metric on the Market-1501 dataset. Furthermore, in this paper, a fully automated data collection strategy from surveillance cameras for self-supervised pre-training is proposed in order to increase the generality of neural networks on real-world data. The use of self-supervised pre-training on the data collected using the proposed strategy improves the quality of cross-domain upper-body Re-ID on the DukeMTMC-reID dataset by 1.0% according to the Rank1 and mAP metrics. Full article
(This article belongs to the Special Issue Person Re-Identification Based on Computer Vision)
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12 pages, 1121 KiB  
Article
Discriminatively Unsupervised Learning Person Re-Identification via Considering Complicated Images
by Rong Quan, Biaoyi Xu and Dong Liang
Sensors 2023, 23(6), 3259; https://doi.org/10.3390/s23063259 - 20 Mar 2023
Cited by 2 | Viewed by 2066
Abstract
State-of-the-art purely unsupervised learning person re-ID methods first cluster all the images into multiple clusters and assign each clustered image a pseudo label based on the cluster result. Then, they construct a memory dictionary that stores all the clustered images, and subsequently train [...] Read more.
State-of-the-art purely unsupervised learning person re-ID methods first cluster all the images into multiple clusters and assign each clustered image a pseudo label based on the cluster result. Then, they construct a memory dictionary that stores all the clustered images, and subsequently train the feature extraction network based on this dictionary. All these methods directly discard the unclustered outliers in the clustering process and train the network only based on the clustered images. The unclustered outliers are complicated images containing different clothes and poses, with low resolution, severe occlusion, and so on, which are common in real-world applications. Therefore, models trained only on clustered images will be less robust and unable to handle complicated images. We construct a memory dictionary that considers complicated images consisting of both clustered and unclustered images, and design a corresponding contrastive loss by considering both kinds of images. The experimental results show that our memory dictionary that considers complicated images and contrastive loss can improve the person re-ID performance, which demonstrates the effectiveness of considering unclustered complicated images in unsupervised person re-ID. Full article
(This article belongs to the Special Issue Person Re-Identification Based on Computer Vision)
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11 pages, 1541 KiB  
Communication
Person Re-Identification with Improved Performance by Incorporating Focal Tversky Loss in AGW Baseline
by Shao-Kang Huang, Chen-Chien Hsu and Wei-Yen Wang
Sensors 2022, 22(24), 9852; https://doi.org/10.3390/s22249852 - 15 Dec 2022
Cited by 6 | Viewed by 2489
Abstract
Person re-identification (re-ID) is one of the essential tasks for modern visual intelligent systems to identify a person from images or videos captured at different times, viewpoints, and spatial positions. In fact, it is easy to make an incorrect estimate for person re-ID [...] Read more.
Person re-identification (re-ID) is one of the essential tasks for modern visual intelligent systems to identify a person from images or videos captured at different times, viewpoints, and spatial positions. In fact, it is easy to make an incorrect estimate for person re-ID in the presence of illumination change, low resolution, and pose differences. To provide a robust and accurate prediction, machine learning techniques are extensively used nowadays. However, learning-based approaches often face difficulties in data imbalance and distinguishing a person from others having strong appearance similarity. To improve the overall re-ID performance, false positives and false negatives should be part of the integral factors in the design of the loss function. In this work, we refine the well-known AGW baseline by incorporating a focal Tversky loss to address the data imbalance issue and facilitate the model to learn effectively from the hard examples. Experimental results show that the proposed re-ID method reaches rank-1 accuracy of 96.2% (with mAP: 94.5) and rank-1 accuracy of 93% (with mAP: 91.4) on Market1501 and DukeMTMC datasets, respectively, outperforming the state-of-the-art approaches. Full article
(This article belongs to the Special Issue Person Re-Identification Based on Computer Vision)
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19 pages, 558 KiB  
Article
Unsupervised Person Re-Identification with Attention-Guided Fine-Grained Features and Symmetric Contrast Learning
by Yongzhi Wu, Wenzhong Yang and Mengting Wang
Sensors 2022, 22(18), 6978; https://doi.org/10.3390/s22186978 - 15 Sep 2022
Cited by 3 | Viewed by 2836
Abstract
Unsupervised person re-identification has attracted a lot of attention due to its strong potential to adapt to new environments without manual annotation, but learning to recognise features in disjoint camera views without annotation is still challenging. Existing studies tend to ignore the optimisation [...] Read more.
Unsupervised person re-identification has attracted a lot of attention due to its strong potential to adapt to new environments without manual annotation, but learning to recognise features in disjoint camera views without annotation is still challenging. Existing studies tend to ignore the optimisation of feature extractors in the feature-extraction stage of this task, while the use of traditional losses in the unsupervised learning stage severely affects the performance of the model. Additionally the use of a contrast learning framework in the latest methods uses only a single cluster centre or all instance features, without considering the correctness and diversity of the samples in the class, which affects the training of the model. Therefore, in this paper, we design an unsupervised person-re-identification framework called attention-guided fine-grained feature network and symmetric contrast learning (AFF_SCL) to improve the two stages in the unsupervised person-re-identification task. AFF_SCL focuses on learning recognition features through two key modules, namely the Attention-guided Fine-grained Feature network (AFF) and the Symmetric Contrast Learning module (SCL). Specifically, the attention-guided fine-grained feature network enhances the network’s ability to discriminate pedestrians by performing further attention operations on fine-grained features to obtain detailed features of pedestrians. The symmetric contrast learning module replaces the traditional loss function to exploit the information potential given by the multiple samples and maintains the stability and generalisation capability of the model. The performance of the USL and UDA methods is tested on the Market-1501 and DukeMTMC-reID datasets by means of the results, which demonstrate that the method outperforms some existing methods, indicating the superiority of the framework. Full article
(This article belongs to the Special Issue Person Re-Identification Based on Computer Vision)
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