Tyre pattern image retrieval – current status and challenges
Tyre pattern image retrieval (TPIR) is an important tool in the investigation of criminal activities and traffic accidents. Although content-based image retrieval (CBIR) has been developed for decades with abundant results, the study on TPIR which started in the 1990s has not made much progress. The...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2021-04-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2020.1806207 |
| _version_ | 1797684094119706624 |
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| author | Liu Ying Liu Qiqi Fan Jiulun Wang Fuping Fu Jianlong Yuan Qingan Chiew Tuan Kiang Ling Nam |
| author_facet | Liu Ying Liu Qiqi Fan Jiulun Wang Fuping Fu Jianlong Yuan Qingan Chiew Tuan Kiang Ling Nam |
| author_sort | Liu Ying |
| collection | DOAJ |
| description | Tyre pattern image retrieval (TPIR) is an important tool in the investigation of criminal activities and traffic accidents. Although content-based image retrieval (CBIR) has been developed for decades with abundant results, the study on TPIR which started in the 1990s has not made much progress. The lack of large standard test datasets is a crucial shortcoming which limits the research in this field. Information presented in this paper is a result of the authors’ literature research on recent academic publications and practical field investigation in the public security and transportation sectors. The state-of-the-art technologies in the field of TPIR are surveyed in detail from two aspects of tyre patterns – their low-level spatial features and high-level semantic features. Existing algorithms are examined and their pros and cons are compared and verified through experimental results. This paper also surveys the available tyre pattern datasets used in all available literature. Finally, with the considerations on technology trends in image retrieval and application requirements in TPIR, the future research directions in this field are laid out. |
| first_indexed | 2024-03-12T00:24:38Z |
| format | Article |
| id | doaj.art-c47aeafef3474f74ab53e12e2c6ba43e |
| institution | Directory Open Access Journal |
| issn | 0954-0091 1360-0494 |
| language | English |
| last_indexed | 2024-03-12T00:24:38Z |
| publishDate | 2021-04-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj.art-c47aeafef3474f74ab53e12e2c6ba43e2023-09-15T10:47:59ZengTaylor & Francis GroupConnection Science0954-00911360-04942021-04-0133223725510.1080/09540091.2020.18062071806207Tyre pattern image retrieval – current status and challengesLiu Ying0Liu Qiqi1Fan Jiulun2Wang Fuping3Fu Jianlong4Yuan Qingan5Chiew Tuan Kiang6Ling Nam7Center for Image and Information Processing, Xi’an University of Posts and TelecommunicationsCenter for Image and Information Processing, Xi’an University of Posts and TelecommunicationsCenter for Image and Information Processing, Xi’an University of Posts and TelecommunicationsCenter for Image and Information Processing, Xi’an University of Posts and TelecommunicationsMicrosoft Research AsiaCriminal Investigation Bureau of Shaanxi Public Security DepartmentRekindle Pte LtdDepartment of Computer Science and Engineering, Santa Clara UniversityTyre pattern image retrieval (TPIR) is an important tool in the investigation of criminal activities and traffic accidents. Although content-based image retrieval (CBIR) has been developed for decades with abundant results, the study on TPIR which started in the 1990s has not made much progress. The lack of large standard test datasets is a crucial shortcoming which limits the research in this field. Information presented in this paper is a result of the authors’ literature research on recent academic publications and practical field investigation in the public security and transportation sectors. The state-of-the-art technologies in the field of TPIR are surveyed in detail from two aspects of tyre patterns – their low-level spatial features and high-level semantic features. Existing algorithms are examined and their pros and cons are compared and verified through experimental results. This paper also surveys the available tyre pattern datasets used in all available literature. Finally, with the considerations on technology trends in image retrieval and application requirements in TPIR, the future research directions in this field are laid out.http://dx.doi.org/10.1080/09540091.2020.1806207tyre pattern retrievaltyre pattern data setslow-level featureshigh-level semantic featuresfuture research direction |
| spellingShingle | Liu Ying Liu Qiqi Fan Jiulun Wang Fuping Fu Jianlong Yuan Qingan Chiew Tuan Kiang Ling Nam Tyre pattern image retrieval – current status and challenges Connection Science tyre pattern retrieval tyre pattern data sets low-level features high-level semantic features future research direction |
| title | Tyre pattern image retrieval – current status and challenges |
| title_full | Tyre pattern image retrieval – current status and challenges |
| title_fullStr | Tyre pattern image retrieval – current status and challenges |
| title_full_unstemmed | Tyre pattern image retrieval – current status and challenges |
| title_short | Tyre pattern image retrieval – current status and challenges |
| title_sort | tyre pattern image retrieval current status and challenges |
| topic | tyre pattern retrieval tyre pattern data sets low-level features high-level semantic features future research direction |
| url | http://dx.doi.org/10.1080/09540091.2020.1806207 |
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