Fast Traffic Sign Recognition with a Rotation Invariant Binary Pattern Based Feature
Robust and fast traffic sign recognition is very important but difficult for safe driving assistance systems. This study addresses fast and robust traffic sign recognition to enhance driving safety. The proposed method includes three stages. First, a typical Hough transformation is adopted to implem...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2015-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/15/1/2161 |
| _version_ | 1798006946529280000 |
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| author | Shouyi Yin Peng Ouyang Leibo Liu Yike Guo Shaojun Wei |
| author_facet | Shouyi Yin Peng Ouyang Leibo Liu Yike Guo Shaojun Wei |
| author_sort | Shouyi Yin |
| collection | DOAJ |
| description | Robust and fast traffic sign recognition is very important but difficult for safe driving assistance systems. This study addresses fast and robust traffic sign recognition to enhance driving safety. The proposed method includes three stages. First, a typical Hough transformation is adopted to implement coarse-grained location of the candidate regions of traffic signs. Second, a RIBP (Rotation Invariant Binary Pattern) based feature in the affine and Gaussian space is proposed to reduce the time of traffic sign detection and achieve robust traffic sign detection in terms of scale, rotation, and illumination. Third, the techniques of ANN (Artificial Neutral Network) based feature dimension reduction and classification are designed to reduce the traffic sign recognition time. Compared with the current work, the experimental results in the public datasets show that this work achieves robustness in traffic sign recognition with comparable recognition accuracy and faster processing speed, including training speed and recognition speed. |
| first_indexed | 2024-04-11T13:02:31Z |
| format | Article |
| id | doaj.art-34e9be618c7346f2b614fdc1d19a3a67 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:02:31Z |
| publishDate | 2015-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-34e9be618c7346f2b614fdc1d19a3a672022-12-22T04:22:52ZengMDPI AGSensors1424-82202015-01-011512161218010.3390/s150102161s150102161Fast Traffic Sign Recognition with a Rotation Invariant Binary Pattern Based FeatureShouyi Yin0Peng Ouyang1Leibo Liu2Yike Guo3Shaojun Wei4Institute of Microelectronics, Tsinghua University, Beijing 100084, ChinaInstitute of Microelectronics, Tsinghua University, Beijing 100084, ChinaInstitute of Microelectronics, Tsinghua University, Beijing 100084, ChinaDepartment of Computing, Imperial College, London SW7 2AZ, UKInstitute of Microelectronics, Tsinghua University, Beijing 100084, ChinaRobust and fast traffic sign recognition is very important but difficult for safe driving assistance systems. This study addresses fast and robust traffic sign recognition to enhance driving safety. The proposed method includes three stages. First, a typical Hough transformation is adopted to implement coarse-grained location of the candidate regions of traffic signs. Second, a RIBP (Rotation Invariant Binary Pattern) based feature in the affine and Gaussian space is proposed to reduce the time of traffic sign detection and achieve robust traffic sign detection in terms of scale, rotation, and illumination. Third, the techniques of ANN (Artificial Neutral Network) based feature dimension reduction and classification are designed to reduce the traffic sign recognition time. Compared with the current work, the experimental results in the public datasets show that this work achieves robustness in traffic sign recognition with comparable recognition accuracy and faster processing speed, including training speed and recognition speed.http://www.mdpi.com/1424-8220/15/1/2161traffic sign recognitionbinary patternSIFTartificial neutral network |
| spellingShingle | Shouyi Yin Peng Ouyang Leibo Liu Yike Guo Shaojun Wei Fast Traffic Sign Recognition with a Rotation Invariant Binary Pattern Based Feature Sensors traffic sign recognition binary pattern SIFT artificial neutral network |
| title | Fast Traffic Sign Recognition with a Rotation Invariant Binary Pattern Based Feature |
| title_full | Fast Traffic Sign Recognition with a Rotation Invariant Binary Pattern Based Feature |
| title_fullStr | Fast Traffic Sign Recognition with a Rotation Invariant Binary Pattern Based Feature |
| title_full_unstemmed | Fast Traffic Sign Recognition with a Rotation Invariant Binary Pattern Based Feature |
| title_short | Fast Traffic Sign Recognition with a Rotation Invariant Binary Pattern Based Feature |
| title_sort | fast traffic sign recognition with a rotation invariant binary pattern based feature |
| topic | traffic sign recognition binary pattern SIFT artificial neutral network |
| url | http://www.mdpi.com/1424-8220/15/1/2161 |
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