Real‐time embedded implementation of robust speed‐limit sign recognition using a novel centroid‐to‐contour description method
Traffic sign recognition is a very important function in automatic driving assistance systems (ADAS). This study addresses the design and implementation of a vision‐based ADAS based on an image‐based speed‐limit sign (SLS) recognition algorithm, which can automatically detect and recognise SLS on th...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-09-01
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| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2016.0082 |
| _version_ | 1797684181713551360 |
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| author | Chi‐Yi Tsai Hsien‐Chen Liao Kuang‐Jui Hsu |
| author_facet | Chi‐Yi Tsai Hsien‐Chen Liao Kuang‐Jui Hsu |
| author_sort | Chi‐Yi Tsai |
| collection | DOAJ |
| description | Traffic sign recognition is a very important function in automatic driving assistance systems (ADAS). This study addresses the design and implementation of a vision‐based ADAS based on an image‐based speed‐limit sign (SLS) recognition algorithm, which can automatically detect and recognise SLS on the road in real‐time. To improve the recognition rate of SLS having different orientations and scales in the image, this study also presents a new sign content description algorithm, which describes the detected road sign using centroid‐to‐contour (CtC) distances of the extracted sign content. The proposed CtC descriptor is robust to translation, rotation and scale changes of the SLS in the image. This advantage improves the recognition accuracy of a support vector machine classifier trained using a large database of traffic signs. The proposed SLS recognition method had been implemented on two different embedded platforms, each of them equipped with an ARM‐based Quad‐Core CPU running Android 4.4 operating system. Experimental results validate that the proposed method not only provides a high recognition rate, but also achieves real‐time performance up to 30 frames per second for processing 1280 × 720 video streams running on a commercial ARM‐based smartphone. |
| first_indexed | 2024-03-12T00:25:51Z |
| format | Article |
| id | doaj.art-125f8d90dfd244f482c164601da2db12 |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:25:51Z |
| publishDate | 2017-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-125f8d90dfd244f482c164601da2db122023-09-15T10:36:02ZengWileyIET Computer Vision1751-96321751-96402017-09-0111640741410.1049/iet-cvi.2016.0082Real‐time embedded implementation of robust speed‐limit sign recognition using a novel centroid‐to‐contour description methodChi‐Yi Tsai0Hsien‐Chen Liao1Kuang‐Jui Hsu2Department of Electrical and Computer EngineeringTamkang University151 Ying‐chuan Road, Danshui DistrictNew Taipei City251TaiwanDepartment of Electrical and Computer EngineeringTamkang University151 Ying‐chuan Road, Danshui DistrictNew Taipei City251TaiwanDepartment of Electrical and Computer EngineeringTamkang University151 Ying‐chuan Road, Danshui DistrictNew Taipei City251TaiwanTraffic sign recognition is a very important function in automatic driving assistance systems (ADAS). This study addresses the design and implementation of a vision‐based ADAS based on an image‐based speed‐limit sign (SLS) recognition algorithm, which can automatically detect and recognise SLS on the road in real‐time. To improve the recognition rate of SLS having different orientations and scales in the image, this study also presents a new sign content description algorithm, which describes the detected road sign using centroid‐to‐contour (CtC) distances of the extracted sign content. The proposed CtC descriptor is robust to translation, rotation and scale changes of the SLS in the image. This advantage improves the recognition accuracy of a support vector machine classifier trained using a large database of traffic signs. The proposed SLS recognition method had been implemented on two different embedded platforms, each of them equipped with an ARM‐based Quad‐Core CPU running Android 4.4 operating system. Experimental results validate that the proposed method not only provides a high recognition rate, but also achieves real‐time performance up to 30 frames per second for processing 1280 × 720 video streams running on a commercial ARM‐based smartphone.https://doi.org/10.1049/iet-cvi.2016.0082real-time embedded implementationcentroid-to-contour description methodtraffic sign recognitionautomatic driving assistance systemvision-based ADASimage-based speed-limit sign recognition algorithm |
| spellingShingle | Chi‐Yi Tsai Hsien‐Chen Liao Kuang‐Jui Hsu Real‐time embedded implementation of robust speed‐limit sign recognition using a novel centroid‐to‐contour description method IET Computer Vision real-time embedded implementation centroid-to-contour description method traffic sign recognition automatic driving assistance system vision-based ADAS image-based speed-limit sign recognition algorithm |
| title | Real‐time embedded implementation of robust speed‐limit sign recognition using a novel centroid‐to‐contour description method |
| title_full | Real‐time embedded implementation of robust speed‐limit sign recognition using a novel centroid‐to‐contour description method |
| title_fullStr | Real‐time embedded implementation of robust speed‐limit sign recognition using a novel centroid‐to‐contour description method |
| title_full_unstemmed | Real‐time embedded implementation of robust speed‐limit sign recognition using a novel centroid‐to‐contour description method |
| title_short | Real‐time embedded implementation of robust speed‐limit sign recognition using a novel centroid‐to‐contour description method |
| title_sort | real time embedded implementation of robust speed limit sign recognition using a novel centroid to contour description method |
| topic | real-time embedded implementation centroid-to-contour description method traffic sign recognition automatic driving assistance system vision-based ADAS image-based speed-limit sign recognition algorithm |
| url | https://doi.org/10.1049/iet-cvi.2016.0082 |
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