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 |
| Summary: | 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. |
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| ISSN: | 0954-0091 1360-0494 |