A Study on Deep Convolutional Neural Network Based Approaches for Person Re-identification

Chahar, Harendra; Nain, Neeta

doi:10.1007/978-3-319-69900-4_69

Cited by 4 publications

(5 citation statements)

References 28 publications

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“…Covering Analysis [12] Handcrafted person Re-ID Deep learning person Re-ID Applications and methods [13,14,15,16,17,18,19,20] Deep learning person Re-ID (mainly supervised)…”

Section: Surveymentioning

confidence: 99%

Unsupervised Person Re-Identification: A Systematic Survey of Challenges and Solutions

Lin¹,

Ren²,

Yeh³

et al. 2021

Preprint

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Person re-identification (Re-ID) has been a significant research topic in the past decade due to its real-world applications and research significance. While supervised person Re-ID methods achieve superior performance over unsupervised counterparts, they can not scale to large unlabelled datasets and new domains due to the prohibitive labelling cost. Therefore, unsupervised person Re-ID has drawn increasing attention for its potential to address the scalability issue in person Re-ID. Unsupervised person Re-ID is challenging primarily due to lacking identity labels to supervise person feature representation learning. The corresponding solutions are diverse and complex, with various merits and limitations. Therefore, comprehensive surveys on this topic are essential to summarise challenges and solutions to foster future research. Existing person Re-ID surveys have focused on supervised methods from classifications and applications. Still, they lack detailed discussion on how the person Re-ID solutions address the underlying person Re-Id challenges. This survey review recent works on unsupervised person Re-ID from the perspective of challenges and solutions. Specifically, we provide an in-depth analysis of highly influential methods considering the four significant challenges in unsupervised person Re-ID: 1) lacking ground-truth identity labels to supervise person feature learning; 2) learning discriminative person features with pseudo-supervision; 3) learning crosscamera invariant person features and 4) the domain gap between datasets. We summarise and analyze evaluation results and provide insights on the effectiveness of the solutions. Finally, we discuss open issues and suggest some promising future research directions.

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“…Covering Analysis [12] Handcrafted person Re-ID Deep learning person Re-ID Applications and methods [13,14,15,16,17,18,19,20] Deep learning person Re-ID (mainly supervised)…”

Section: Surveymentioning

confidence: 99%

Unsupervised Person Re-Identification: A Systematic Survey of Challenges and Solutions

Lin¹,

Ren²,

Yeh³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Prior to this survey, some researchers [4,5,42,44,46,46,47,81,111,116,118,121,127,131,137,150] have also reviewed person Re-ID field. In Table 1, we summarize the major contributions of these reviews.…”

Section: Introductionmentioning

confidence: 99%

“…In Table 1, we summarize the major contributions of these reviews. Some of these surveys [5,150] summarized image-based and video-based person Re-ID methods. Other surveys [46,81,111,121,131,137] summarized deep learning-based person Re-ID method in different dimensions, which has developed rapidly after 2014 and become the main research means.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based person re-identification methods: A survey and outlook of recent works

Ming¹,

Zhu²,

Wang³

et al. 2021

Preprint

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The main studys of person Re-ID surveys have been summarized in recent years.• Deep learning-based person Re-ID methods are classified into five categories according to their characteristic.• The above five categories are subdivided according to their technique types.• This classification is more suitable for researchers to explore these methods from their practical needs.• Furthermore, five possible research directions are analyzed for person Re-ID researchers.

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“…Prior to this survey, some researchers [1], [9], [10], [127], [178]- [187], [189]- [191] have also reviewed re-identification field. In Table 1, we summarize the major contributions of these reviews.…”

Section: Introductionmentioning

confidence: 99%

“…Some of these surveys [179], [180] summarized the person re-identification before the outbreak of deep learning, which has developed rapidly after 2014 and become the main research means. Other surveys summarized these outcomes based solely on datatype [1], [9], [191], framework [127], or performance evaluation [178], or reviewed the heterogeneous person re-identification methods [181]. Recently, Wu et al [10] have outlined the person re-identification datasets, loss functions and methods.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Overview of Person Re-Identification Approaches

Wang

Zhao

et al. 2020

IEEE Access

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Person re-identification, identifying and tracking pedestrians in cross-domain monitoring systems, is an important technology in the computer vision field and of real significance for the construction of smart cities. With the development of deep learning techniques, especially convolutional neural networks, this technology has received more extensive attention and improvement in recent years and a large number of noteworthy achievements have emerged. This paper provides a comprehensive overview of person re-identification approaches to assist researchers in quickly understand this field with preference as well as to provide a more structured framework. By reviewing more than 300 re-identification related papers, the focus of these studies is summarized as information extraction, metric learning, post-processing, efficiency improvement, labeling cost reduction, and data type expansion. This classification is then organized based on different technologies, and on this basis, the pros and cons of each technology are analyzed. Moreover, this overview summarizes the difficulties and challenges of re-identification and discusses the possible research directions for reference. INDEX TERMSComputer vision, convolutional neural networks, distance learning, feature extraction, person re-identification.

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A Study on Deep Convolutional Neural Network Based Approaches for Person Re-identification

Cited by 4 publications

References 28 publications

Unsupervised Person Re-Identification: A Systematic Survey of Challenges and Solutions

Unsupervised Person Re-Identification: A Systematic Survey of Challenges and Solutions

Deep learning-based person re-identification methods: A survey and outlook of recent works

A Comprehensive Overview of Person Re-Identification Approaches

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