A Survey on Deep Learning-Based Person Re-Identification Systems

Almasawa, Muna O.; Elrefaei, Lamiaa A.; Moria, Kawthar

doi:10.1109/access.2019.2957336

Cited by 35 publications

(20 citation statements)

References 98 publications

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“…A probe is mostly used to extract features and pass these to recognition system, which either recognizes if similarity score is good with one or more images/videos or it asks to add a new identity of a pedestrian for future detection. A survey is presented by Muna et al in [29].…”

Section: Learning-based Approachesmentioning

confidence: 99%

Video Based Person Re Identification Methods, Datasets, and Deep Learning

Talware¹,

Koli²

2020

IJEAT

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Video Analytics applications like security and surveillance face a critical problem of person re-identification abbreviated as re-ID. The last decade witnessed the emergence of large-scale datasets and deep learning methods to use these huge data volumes. Most current re-ID methods are classified into either image-based or video-based re-ID. Matching persons across multiple camera views have attracted lots of recent research attention. Feature representation and metric learning are major issues for person re-identification. The focus of re-ID work is now shifting towards developing end-to-end re-Id and tracking systems for practical use with dynamic datasets. Most previous works contributed to the significant progress of person re-identification on still images using image retrieval models. This survey considers the more informative and challenging video-based person re-ID problem, pedestrian re-ID in particular. Publicly available datasets and codes are listed as a part of this work. Current trends which include open re-identification systems, use of discriminative features and deep learning is marching towards new applications in security and surveillance, typically for tracking

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Section: Learning-based Approachesmentioning

confidence: 99%

Video Based Person Re Identification Methods, Datasets, and Deep Learning

Talware¹,

Koli²

2020

IJEAT

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“…P ERSON re-identification [1]- [3], which aims to find a given person from non-overlapping cameras, is a research hotspot in the computer vision field with great progress. Due to changes of human pose, viewpoint, illumination condition, resolution and so on, there are still improvements in person re-identification.…”

Section: Introductionmentioning

confidence: 99%

Real-World Person Re-Identification via Super-Resolution and Semi-Supervised Methods

Xia

Zhu

2021

IEEE Access

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Person re-identification has made great progress over the years. However, due to the problem of super-resolution and few labeled samples, it is difficult to apply in practice. In this paper, we propose a semi-supervised super-resolution person re-identification method based on soft multi-labels. Firstly, a Mixed-Space Super-Resolution model (MSSR) is constructed based on Generative Adversarial Networks (GAN), which aims to convert low-resolution person images into high-resolution images. Secondly, a Partbased Graph Convolutional Network (PGCN) is proposed to extract discriminative feature by exploring the relationship of local features within person. Finally, to solve the problem of label limitation, we use the PGCN trained with a small amount of labeled samples to predict the soft multi-labels of unlabeled samples, and further train PGCN with unlabeled samples based on a novel multi-label similarity loss. Experiments have been conducted on the Market1501, CUHK03, and MSMT17 datasets to evaluate this method, which show that it outperforms other semi-supervised methods.

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“…Anyway, despite the efforts of many computer vision researchers in this application area, person re-identification task still presents several problems largely unsolved. Most of the person re-identification methods, in fact, are based on visual features extracted from images to model a person’s appearance [ 26 , 27 ]. This leads to a first class of problems since, as known, visual features have many weaknesses, including illumination changes, shadows, direction of light, and many others.…”

Section: Introductionmentioning

confidence: 99%

Bodyprint—A Meta-Feature Based LSTM Hashing Model for Person Re-Identification

Avola

Cinque

Fagioli

et al. 2020

Sensors

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Person re-identification is concerned with matching people across disjointed camera views at different places and different time instants. This task results of great interest in computer vision, especially in video surveillance applications where the re-identification and tracking of persons are required on uncontrolled crowded spaces and after long time periods. The latter aspects are responsible for most of the current unsolved problems of person re-identification, in fact, the presence of many people in a location as well as the passing of hours or days give arise to important visual appearance changes of people, for example, clothes, lighting, and occlusions; thus making person re-identification a very hard task. In this paper, for the first time in the state-of-the-art, a meta-feature based Long Short-Term Memory (LSTM) hashing model for person re-identification is presented. Starting from 2D skeletons extracted from RGB video streams, the proposed method computes a set of novel meta-features based on movement, gait, and bone proportions. These features are analysed by a network composed of a single LSTM layer and two dense layers. The first layer is used to create a pattern of the person’s identity, then, the seconds are used to generate a bodyprint hash through binary coding. The effectiveness of the proposed method is tested on three challenging datasets, that is, iLIDS-VID, PRID 2011, and MARS. In particular, the reported results show that the proposed method, which is not based on visual appearance of people, is fully competitive with respect to other methods based on visual features. In addition, thanks to its skeleton model abstraction, the method results to be a concrete contribute to address open problems, such as long-term re-identification and severe illumination changes, which tend to heavily influence the visual appearance of persons.

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A Survey on Deep Learning-Based Person Re-Identification Systems

Cited by 35 publications

References 98 publications

Video Based Person Re Identification Methods, Datasets, and Deep Learning

Video Based Person Re Identification Methods, Datasets, and Deep Learning

Real-World Person Re-Identification via Super-Resolution and Semi-Supervised Methods

Bodyprint—A Meta-Feature Based LSTM Hashing Model for Person Re-Identification

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