Automatic Roadside Camera Calibration with Transformers
Previous camera self-calibration methods have exhibited certain notable shortcomings. On the one hand, they either exclusively emphasized scene cues or solely focused on vehicle-related cues, resulting in a lack of adaptability to diverse scenarios and a limited number of effective features. Further...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/23/9527 |
| _version_ | 1797399599848095744 |
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| author | Yong Li Zhiguo Zhao Yunli Chen Xiaoting Zhang Rui Tian |
| author_facet | Yong Li Zhiguo Zhao Yunli Chen Xiaoting Zhang Rui Tian |
| author_sort | Yong Li |
| collection | DOAJ |
| description | Previous camera self-calibration methods have exhibited certain notable shortcomings. On the one hand, they either exclusively emphasized scene cues or solely focused on vehicle-related cues, resulting in a lack of adaptability to diverse scenarios and a limited number of effective features. Furthermore, these methods either solely utilized geometric features within traffic scenes or exclusively extracted semantic information, failing to comprehensively consider both aspects. This limited the comprehensive feature extraction from scenes, ultimately leading to a decrease in calibration accuracy. Additionally, conventional vanishing point-based self-calibration methods often required the design of additional edge-background models and manual parameter tuning, thereby increasing operational complexity and the potential for errors. Given these observed limitations, and in order to address these challenges, we propose an innovative roadside camera self-calibration model based on the Transformer architecture. This model possesses a unique capability to simultaneously learn scene features and vehicle features within traffic scenarios while considering both geometric and semantic information. Through this approach, our model can overcome the constraints of prior methods, enhancing calibration accuracy and robustness while reducing operational complexity and the potential for errors. Our method outperforms existing approaches on both real-world dataset scenarios and publicly available datasets, demonstrating the effectiveness of our approach. |
| first_indexed | 2024-03-09T01:42:30Z |
| format | Article |
| id | doaj.art-d77de39c514342d6a71156a80d89e63a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T01:42:30Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d77de39c514342d6a71156a80d89e63a2023-12-08T15:26:18ZengMDPI AGSensors1424-82202023-11-012323952710.3390/s23239527Automatic Roadside Camera Calibration with TransformersYong Li0Zhiguo Zhao1Yunli Chen2Xiaoting Zhang3Rui Tian4The Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaThe Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaThe Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaThe Faculty of Science Technology, Beijing University of Technology, Beijing 100124, ChinaThe Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaPrevious camera self-calibration methods have exhibited certain notable shortcomings. On the one hand, they either exclusively emphasized scene cues or solely focused on vehicle-related cues, resulting in a lack of adaptability to diverse scenarios and a limited number of effective features. Furthermore, these methods either solely utilized geometric features within traffic scenes or exclusively extracted semantic information, failing to comprehensively consider both aspects. This limited the comprehensive feature extraction from scenes, ultimately leading to a decrease in calibration accuracy. Additionally, conventional vanishing point-based self-calibration methods often required the design of additional edge-background models and manual parameter tuning, thereby increasing operational complexity and the potential for errors. Given these observed limitations, and in order to address these challenges, we propose an innovative roadside camera self-calibration model based on the Transformer architecture. This model possesses a unique capability to simultaneously learn scene features and vehicle features within traffic scenarios while considering both geometric and semantic information. Through this approach, our model can overcome the constraints of prior methods, enhancing calibration accuracy and robustness while reducing operational complexity and the potential for errors. Our method outperforms existing approaches on both real-world dataset scenarios and publicly available datasets, demonstrating the effectiveness of our approach.https://www.mdpi.com/1424-8220/23/23/9527camera calibrationvanishing point detectiontransformer |
| spellingShingle | Yong Li Zhiguo Zhao Yunli Chen Xiaoting Zhang Rui Tian Automatic Roadside Camera Calibration with Transformers Sensors camera calibration vanishing point detection transformer |
| title | Automatic Roadside Camera Calibration with Transformers |
| title_full | Automatic Roadside Camera Calibration with Transformers |
| title_fullStr | Automatic Roadside Camera Calibration with Transformers |
| title_full_unstemmed | Automatic Roadside Camera Calibration with Transformers |
| title_short | Automatic Roadside Camera Calibration with Transformers |
| title_sort | automatic roadside camera calibration with transformers |
| topic | camera calibration vanishing point detection transformer |
| url | https://www.mdpi.com/1424-8220/23/23/9527 |
| work_keys_str_mv | AT yongli automaticroadsidecameracalibrationwithtransformers AT zhiguozhao automaticroadsidecameracalibrationwithtransformers AT yunlichen automaticroadsidecameracalibrationwithtransformers AT xiaotingzhang automaticroadsidecameracalibrationwithtransformers AT ruitian automaticroadsidecameracalibrationwithtransformers |