A Review on Person Re-Identification Techniques and Its Analysis
Person re-identification (Re-ID) emerges as a captivating realm within computer vision, dedicated to the task of recognizing the same individual across diverse camera angles or locations. The realm of video-based person re-identification (video re-ID) has recently captivated increasing interest, owi...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10858128/ |
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| author | C. Selvan H. Anwar Basha K. Meenakshi Soumyalatha Naveen |
| author_facet | C. Selvan H. Anwar Basha K. Meenakshi Soumyalatha Naveen |
| author_sort | C. Selvan |
| collection | DOAJ |
| description | Person re-identification (Re-ID) emerges as a captivating realm within computer vision, dedicated to the task of recognizing the same individual across diverse camera angles or locations. The realm of video-based person re-identification (video re-ID) has recently captivated increasing interest, owing to its wide array of practical applications spanning surveillance, smart city solutions, and public safety measures. Nevertheless, video re-ID proves to be a formidable challenge, an ever-evolving domain fraught with a multitude of uncertainties like viewpoint variations, occlusions, pose changes, and unpredictable video sequences. Over the past few years, the realm of deep learning applied to video re-ID has consistently delivered remarkable outcomes on public datasets, showcasing a range of innovative strategies devised to tackle the array of issues encountered in video re-ID. In stark contrast to image-based re-ID, video re-ID stands out as significantly more intricate and demanding. In a bid to inspire forthcoming research endeavors and confronts emerging challenges, this paper presents a comprehensive overview of the latest advancements in deep learning methodologies tailored for video re-ID. It delves into three crucial facets; encompassing succinct explanations of video re-ID techniques along with their constraints, pivotal breakthroughs coupled with the technical hurdles faced, and the architectural framework underpinning these developments. The paper further furnishes a comparative analysis of performance across diverse datasets, offers insightful guidance on enhancing video re-ID strategies, and outlines compelling avenues for future research exploration. |
| format | Article |
| id | doaj-art-d8dad5bd594d46978bda9e23ed74e3a6 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d8dad5bd594d46978bda9e23ed74e3a62025-02-06T00:00:48ZengIEEEIEEE Access2169-35362025-01-0113221332214510.1109/ACCESS.2025.353647810858128A Review on Person Re-Identification Techniques and Its AnalysisC. Selvan0https://orcid.org/0000-0003-4381-0284H. Anwar Basha1https://orcid.org/0000-0001-9002-6316K. Meenakshi2https://orcid.org/0000-0003-4495-6744Soumyalatha Naveen3https://orcid.org/0000-0001-9552-3047School of Computer Science and Engineering, REVA University, Bengaluru, Karnataka, IndiaDepartment of Computer Science and Engineering, Rajalakshmi Institute of Technology, Chennai, Tamil Nadu, IndiaDepartment of Computer Science and Engineering, Vadapalani Campus, SRM Institute of Science and Technology, Chennai, Tamil Nadu, IndiaDepartment of Computer Science and Engineering, Manipal Academy of Higher Education, Manipal Institute of Technology Bengaluru, Manipal, IndiaPerson re-identification (Re-ID) emerges as a captivating realm within computer vision, dedicated to the task of recognizing the same individual across diverse camera angles or locations. The realm of video-based person re-identification (video re-ID) has recently captivated increasing interest, owing to its wide array of practical applications spanning surveillance, smart city solutions, and public safety measures. Nevertheless, video re-ID proves to be a formidable challenge, an ever-evolving domain fraught with a multitude of uncertainties like viewpoint variations, occlusions, pose changes, and unpredictable video sequences. Over the past few years, the realm of deep learning applied to video re-ID has consistently delivered remarkable outcomes on public datasets, showcasing a range of innovative strategies devised to tackle the array of issues encountered in video re-ID. In stark contrast to image-based re-ID, video re-ID stands out as significantly more intricate and demanding. In a bid to inspire forthcoming research endeavors and confronts emerging challenges, this paper presents a comprehensive overview of the latest advancements in deep learning methodologies tailored for video re-ID. It delves into three crucial facets; encompassing succinct explanations of video re-ID techniques along with their constraints, pivotal breakthroughs coupled with the technical hurdles faced, and the architectural framework underpinning these developments. The paper further furnishes a comparative analysis of performance across diverse datasets, offers insightful guidance on enhancing video re-ID strategies, and outlines compelling avenues for future research exploration.https://ieeexplore.ieee.org/document/10858128/Person re-identificationdeep learningvision based re-identificationreview analysis |
| spellingShingle | C. Selvan H. Anwar Basha K. Meenakshi Soumyalatha Naveen A Review on Person Re-Identification Techniques and Its Analysis IEEE Access Person re-identification deep learning vision based re-identification review analysis |
| title | A Review on Person Re-Identification Techniques and Its Analysis |
| title_full | A Review on Person Re-Identification Techniques and Its Analysis |
| title_fullStr | A Review on Person Re-Identification Techniques and Its Analysis |
| title_full_unstemmed | A Review on Person Re-Identification Techniques and Its Analysis |
| title_short | A Review on Person Re-Identification Techniques and Its Analysis |
| title_sort | review on person re identification techniques and its analysis |
| topic | Person re-identification deep learning vision based re-identification review analysis |
| url | https://ieeexplore.ieee.org/document/10858128/ |
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