{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T16:59:57Z","timestamp":1783529997186,"version":"3.55.0"},"reference-count":27,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2015,1,19]],"date-time":"2015-01-19T00:00:00Z","timestamp":1421625600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s150102161","type":"journal-article","created":{"date-parts":[[2015,1,19]],"date-time":"2015-01-19T10:53:54Z","timestamp":1421664834000},"page":"2161-2180","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Fast Traffic Sign Recognition with a Rotation Invariant Binary Pattern Based Feature"],"prefix":"10.3390","volume":"15","author":[{"given":"Shouyi","family":"Yin","sequence":"first","affiliation":[{"name":"Institute of Microelectronics, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Ouyang","sequence":"additional","affiliation":[{"name":"Institute of Microelectronics, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leibo","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Microelectronics, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yike","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Computing, Imperial College, London SW7 2AZ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaojun","family":"Wei","sequence":"additional","affiliation":[{"name":"Institute of Microelectronics, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2015,1,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1496","DOI":"10.1109\/TCE.2009.5278018","article-title":"Efficient embedded speech recognition for very large vocabulary Mandarin car-navigation systems","volume":"55","author":"Qian","year":"2009","journal-title":"IEEE Trans. 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