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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/44100

Title: Boosted Road Sign Detection and Recognition
Authors: Sin-Yu Chen
Jun-Wei Hsieh
Contributors: 國立臺灣海洋大學:資訊工程學系
NTOU:Department of Computer Science and Engineering
Keywords: Machine learning
Cybernetics
Conferences
Roads
Electrical engineering
Date: 2008-07
Issue Date: 2017-11-14T08:43:26Z
Publisher: 2008 International Conference on Machine Learning and Cybernetics
Abstract: Abstract:This paper presents a boosted system to detect and recognize roads signs from videos. The system first uses the Adaboost algorithm to learn the visual characteristics of road sign. Then, a cascaded structure is then used to detect road signs from videos in real time. After detection, a rectification process is then applied for rectifying different skewed road signs into a normal one. Then, its all embedded texts can be more accurately recognized using their distance maps. On the map, a weighting function is used to balance the importance between a road sign’s inner and outer feature so that its embedded characters can be more accurately recognized. Experimental results have proved the superiority of the proposed method in road sign recognition.
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/44100
Appears in Collections:[資訊工程學系] 演講及研討會

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