ROAD SIGNS DETECTION AND RECOGNITION UTILIZING IMAGES AND 3D POINT CLOUD ACQUIRED BY MOBILE MAPPING SYSTEM
High-definition and highly accurate road maps are necessary for the realization of automated driving, and road signs are among the most important element in the road map. Therefore, a technique is necessary which can acquire information about all kinds of road signs automatically and efficiently. Du...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2016-06-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B1/669/2016/isprs-archives-XLI-B1-669-2016.pdf |
| _version_ | 1819012372219559936 |
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| author | Y. H. Li T. Shinohara T. Satoh K. Tachibana |
| author_facet | Y. H. Li T. Shinohara T. Satoh K. Tachibana |
| author_sort | Y. H. Li |
| collection | DOAJ |
| description | High-definition and highly accurate road maps are necessary for the realization of automated driving, and road signs are among the most important element in the road map. Therefore, a technique is necessary which can acquire information about all kinds of road signs automatically and efficiently. Due to the continuous technical advancement of Mobile Mapping System (MMS), it has become possible to acquire large number of images and 3d point cloud efficiently with highly precise position information. In this paper, we present an automatic road sign detection and recognition approach utilizing both images and 3D point cloud acquired by MMS. The proposed approach consists of three stages: 1) detection of road signs from images based on their color and shape features using object based image analysis method, 2) filtering out of over detected candidates utilizing size and position information estimated from 3D point cloud, region of candidates and camera information, and 3) road sign recognition using template matching method after shape normalization. The effectiveness of proposed approach was evaluated by testing dataset, acquired from more than 180 km of different types of roads in Japan. The results show a very high success in detection and recognition of road signs, even under the challenging conditions such as discoloration, deformation and in spite of partial occlusions. |
| first_indexed | 2024-12-21T01:43:00Z |
| format | Article |
| id | doaj.art-15189b53af6d45d3ae19f01ce2b0c719 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-12-21T01:43:00Z |
| publishDate | 2016-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-15189b53af6d45d3ae19f01ce2b0c7192022-12-21T19:20:05ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342016-06-01XLI-B166967310.5194/isprs-archives-XLI-B1-669-2016ROAD SIGNS DETECTION AND RECOGNITION UTILIZING IMAGES AND 3D POINT CLOUD ACQUIRED BY MOBILE MAPPING SYSTEMY. H. Li0T. Shinohara1T. Satoh2K. Tachibana3PASCO Corporation, 2-8-10 Higashiyama, Meguro-ku, Tokyo 153-0043, JapanPASCO Corporation, 2-8-10 Higashiyama, Meguro-ku, Tokyo 153-0043, JapanPASCO Corporation, 2-8-10 Higashiyama, Meguro-ku, Tokyo 153-0043, JapanPASCO Corporation, 2-8-10 Higashiyama, Meguro-ku, Tokyo 153-0043, JapanHigh-definition and highly accurate road maps are necessary for the realization of automated driving, and road signs are among the most important element in the road map. Therefore, a technique is necessary which can acquire information about all kinds of road signs automatically and efficiently. Due to the continuous technical advancement of Mobile Mapping System (MMS), it has become possible to acquire large number of images and 3d point cloud efficiently with highly precise position information. In this paper, we present an automatic road sign detection and recognition approach utilizing both images and 3D point cloud acquired by MMS. The proposed approach consists of three stages: 1) detection of road signs from images based on their color and shape features using object based image analysis method, 2) filtering out of over detected candidates utilizing size and position information estimated from 3D point cloud, region of candidates and camera information, and 3) road sign recognition using template matching method after shape normalization. The effectiveness of proposed approach was evaluated by testing dataset, acquired from more than 180 km of different types of roads in Japan. The results show a very high success in detection and recognition of road signs, even under the challenging conditions such as discoloration, deformation and in spite of partial occlusions.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B1/669/2016/isprs-archives-XLI-B1-669-2016.pdf |
| spellingShingle | Y. H. Li T. Shinohara T. Satoh K. Tachibana ROAD SIGNS DETECTION AND RECOGNITION UTILIZING IMAGES AND 3D POINT CLOUD ACQUIRED BY MOBILE MAPPING SYSTEM The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | ROAD SIGNS DETECTION AND RECOGNITION UTILIZING IMAGES AND 3D POINT CLOUD ACQUIRED BY MOBILE MAPPING SYSTEM |
| title_full | ROAD SIGNS DETECTION AND RECOGNITION UTILIZING IMAGES AND 3D POINT CLOUD ACQUIRED BY MOBILE MAPPING SYSTEM |
| title_fullStr | ROAD SIGNS DETECTION AND RECOGNITION UTILIZING IMAGES AND 3D POINT CLOUD ACQUIRED BY MOBILE MAPPING SYSTEM |
| title_full_unstemmed | ROAD SIGNS DETECTION AND RECOGNITION UTILIZING IMAGES AND 3D POINT CLOUD ACQUIRED BY MOBILE MAPPING SYSTEM |
| title_short | ROAD SIGNS DETECTION AND RECOGNITION UTILIZING IMAGES AND 3D POINT CLOUD ACQUIRED BY MOBILE MAPPING SYSTEM |
| title_sort | road signs detection and recognition utilizing images and 3d point cloud acquired by mobile mapping system |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B1/669/2016/isprs-archives-XLI-B1-669-2016.pdf |
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