Person Re-Identification with RGB–D and RGB–IR Sensors: A Comprehensive Survey
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/3/1504 |
| _version_ | 1797623216866328576 |
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| author | Md Kamal Uddin Amran Bhuiyan Fateha Khanam Bappee Md Matiqul Islam Mahmudul Hasan |
| author_facet | Md Kamal Uddin Amran Bhuiyan Fateha Khanam Bappee Md Matiqul Islam Mahmudul Hasan |
| author_sort | Md Kamal Uddin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T09:25:32Z |
| format | Article |
| id | doaj.art-b19fed4200f0421fbd62ef4ebf60030d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T09:25:32Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b19fed4200f0421fbd62ef4ebf60030d2023-11-16T18:02:06ZengMDPI AGSensors1424-82202023-01-01233150410.3390/s23031504Person Re-Identification with RGB–D and RGB–IR Sensors: A Comprehensive SurveyMd Kamal Uddin0Amran Bhuiyan1Fateha Khanam Bappee2Md Matiqul Islam3Mahmudul Hasan4Interactive Systems Lab, Graduate School of Science and Engineering, Saitama University, Saitama 338-8570, JapanInformation Retrieval and Knowledge Management Research Laboratory, York University, Toronto, ON M3J 1P3, CanadaDepartment of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Noakhali 3814, BangladeshInteractive Systems Lab, Graduate School of Science and Engineering, Saitama University, Saitama 338-8570, JapanInteractive Systems Lab, Graduate School of Science and Engineering, Saitama University, Saitama 338-8570, JapanLearning 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.https://www.mdpi.com/1424-8220/23/3/1504re-identificationvideo surveillancemulti-modalcross-modalRGB–D sensorsRGB–IR sensors |
| spellingShingle | Md Kamal Uddin Amran Bhuiyan Fateha Khanam Bappee Md Matiqul Islam Mahmudul Hasan Person Re-Identification with RGB–D and RGB–IR Sensors: A Comprehensive Survey Sensors re-identification video surveillance multi-modal cross-modal RGB–D sensors RGB–IR sensors |
| title | Person Re-Identification with RGB–D and RGB–IR Sensors: A Comprehensive Survey |
| title_full | Person Re-Identification with RGB–D and RGB–IR Sensors: A Comprehensive Survey |
| title_fullStr | Person Re-Identification with RGB–D and RGB–IR Sensors: A Comprehensive Survey |
| title_full_unstemmed | Person Re-Identification with RGB–D and RGB–IR Sensors: A Comprehensive Survey |
| title_short | Person Re-Identification with RGB–D and RGB–IR Sensors: A Comprehensive Survey |
| title_sort | person re identification with rgb d and rgb ir sensors a comprehensive survey |
| topic | re-identification video surveillance multi-modal cross-modal RGB–D sensors RGB–IR sensors |
| url | https://www.mdpi.com/1424-8220/23/3/1504 |
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