A Comprehensive Overview of Person Re-Identification Approaches

Wang, Hongbo; Du, Haomin; Zhao, Yue; Ji-ming, Yan

doi:10.1109/access.2020.2978344

Cited by 21 publications

(9 citation statements)

References 290 publications

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“…Therefore, we aim to provide a quick understanding and overview of the Re-ID researchers who are interested in working with depth and infrared sensors. There are several remarkable person Re-ID surveys that exist in the literature [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. However, these surveys mainly focus on and give insightful direction on RGB camera-based person Re-ID approaches.…”

Section: Introductionmentioning

confidence: 99%

“…All of these modality-aware, Re-ID works consist of conventional methods [ 14 , 34 , 35 , 36 ] or deep-learning methods [ 37 , 38 , 39 ]. Because all the previous surveys [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ] mainly focus on RGB–RGB single-modality matching, we do not include it in our survey.…”

Section: Introductionmentioning

confidence: 99%

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Person Re-Identification with RGB–D and RGB–IR Sensors: A Comprehensive Survey

Uddin

Bhuiyan

Bappee

et al. 2023

Sensors

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Learning about appearance embedding is of great importance for a variety of different computer-vision applications, which has prompted a surge in person re-identification (Re-ID) papers. The aim of these papers has been to identify an individual over a set of non-overlapping cameras. Despite recent advances in RGB–RGB Re-ID approaches with deep-learning architectures, the approach fails to consistently work well when there are low resolutions in dark conditions. The introduction of different sensors (i.e., RGB–D and infrared (IR)) enables the capture of appearances even in dark conditions. Recently, a lot of research has been dedicated to addressing the issue of finding appearance embedding in dark conditions using different advanced camera sensors. In this paper, we give a comprehensive overview of existing Re-ID approaches that utilize the additional information from different sensor-based methods to address the constraints faced by RGB camera-based person Re-ID systems. Although there are a number of survey papers that consider either the RGB–RGB or Visible-IR scenarios, there are none that consider both RGB–D and RGB–IR. In this paper, we present a detailed taxonomy of the existing approaches along with the existing RGB–D and RGB–IR person Re-ID datasets. Then, we summarize the performance of state-of-the-art methods on several representative RGB–D and RGB–IR datasets. Finally, future directions and current issues are considered for improving the different sensor-based person Re-ID systems.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Person Re-Identification with RGB–D and RGB–IR Sensors: A Comprehensive Survey

Uddin

Bhuiyan

Bappee

et al. 2023

Sensors

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“…It has attracted widespread attention in recent years because of its importance in many practical applications, such as image retrieval and video surveillance analysis. The result of person ReID query can not only help the police to quickly obtain clues of criminal suspects or find missing children, but also enable businesses to capture the patterns of customer behavior and allocate resource by making more reasonable use of commercial value [1].…”

Section: Introductionmentioning

confidence: 99%

Person Re-Identification Based on Attention Mechanism and Context Information Fusion

2021

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Person re-identification (ReID) plays a significant role in video surveillance analysis. In the real world, due to illumination, occlusion, and deformation, pedestrian features extraction is the key to person ReID. Considering the shortcomings of existing methods in pedestrian features extraction, a method based on attention mechanism and context information fusion is proposed. A lightweight attention module is introduced into ResNet50 backbone network equipped with a small number of network parameters, which enhance the significant characteristics of person and suppress irrelevant information. Aiming at the problem of person context information loss due to the over depth of the network, a context information fusion module is designed to sample the shallow feature map of pedestrians and cascade with the high-level feature map. In order to improve the robustness, the model is trained by combining the loss of margin sample mining with the loss function of cross entropy. Experiments are carried out on datasets Market1501 and DukeMTMC-reID, our method achieves rank-1 accuracy of 95.9% on the Market1501 dataset, and 90.1% on the DukeMTMC-reID dataset, outperforming the current mainstream method in case of only using global feature.

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“…The task of person re-identification (ReID) is to identify the target person from a large set collected by multiple nonoverlapping cameras [1]. In recent years, with the development of deep learning, person ReID has attracted extensive attention and research [2]- [5], and has made significant progress in both academia and industry [6], and was applied to the real scenes, such as stations and supermarkets [7]. However, there are some problems in the practical application, mainly including three aspects.…”

Section: Introductionmentioning

confidence: 99%

Person Re-Identification by Effective Features and Self-Optimized Pseudo-Label

Gao

Guan

et al. 2021

IEEE Access

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With the development of deep learning, person re-identification (ReID) has been widely concerned and studied. At present, in practical application, there are three main problems in person ReID: first, it is difficult to locate the target person because the person is frequently partially occluded in crowed scenes; second, it is difficult to match the target person due to the similarity of the target person and other pedestrian features; third, the problem of model performance degradation caused by the large style discrepancies across domain/datasets. These three problems greatly limit the application of person ReID in real scenes. To solve these problems, we proposed a person ReID method based on effective features and self-optimized pseudo-label. Firstly, we designed a feature aggregation module which combines mask channel and pose channel to accurately extract the global saliency features, so as to solve the occlusion problem; secondly, we designed a head-shoulder feature auxiliary module to enhance the feature representation of the head-shoulder, so as to solve the problem of similarity between the target person and other pedestrian features; finally, we designed a self-optimized pseudo-label training module to improves the generalization ability of the model, so as to solve the problem of different styles in the cross-domain environment. Extensive contrast experiments with the state-of-the-art methods on multiple person re-ID datasets show that our method leads to significant improvement, which prove the effectiveness of our method.

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A Comprehensive Overview of Person Re-Identification Approaches

Cited by 21 publications

References 290 publications

Person Re-Identification with RGB–D and RGB–IR Sensors: A Comprehensive Survey

Person Re-Identification with RGB–D and RGB–IR Sensors: A Comprehensive Survey

Person Re-Identification Based on Attention Mechanism and Context Information Fusion

Person Re-Identification by Effective Features and Self-Optimized Pseudo-Label

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