LVQ and HOG based speed limit traffic signs detection and categorization
This conference paper was presented in the International Conference on Informatics, Electronics and Vision, ICIEV 2014; Dhaka; Bangladesh; 23 May 2014 through 24 May 2014 [© 2014 IEEE] The conference paper's definite version is available at: http://dx.doi.org/10.1109/ICIEV.2014.6850741
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© 2014 IEEE Computer Society
2017
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| 在線閱讀: | http://hdl.handle.net/10361/7508 http://dx.doi.org/10.1109/ICIEV.2014.6850741 |
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10361-75082018-07-25T10:28:14Z LVQ and HOG based speed limit traffic signs detection and categorization Biswas, Rubel Tora, Moumita Roy Bhuiyan, Farazul Haque Department of Computer Science and Engineering, BRAC University Circular hough transform HOG LVQ SVM Traffic sign This conference paper was presented in the International Conference on Informatics, Electronics and Vision, ICIEV 2014; Dhaka; Bangladesh; 23 May 2014 through 24 May 2014 [© 2014 IEEE] The conference paper's definite version is available at: http://dx.doi.org/10.1109/ICIEV.2014.6850741 The proper identification of the traffic signs can ensure driving safety and can play a very important role in reducing the number of road accidents significantly. This paper represents a uniform way to detect the speed limit traffic signs and to confirm it by recognizing the sign's speed number. In this system, firstly the red color objects are segmented from an image using LVQ. Secondly, detected circular part is extracted from the color segmented image using bounding box and then Histogram Oriented Gradient (HOG) is used to collect the feature of the extracted part of circular object and finally SVM classifier is applied to train the HOG features of each speed no. into their corresponding classes. In general, the system detects the prohibitory traffic sign in the first place, specifies whether the detected sign is a speed limit sign, and then determines the allowed speed in case the detected sign is a speed limit sign. The SVM classifier was trained with 200 images which were collected in different light conditions. To check the robustness of this system, it was tested against 381 images which contain 361 Speed Limit traffic sign and 30 Non- Speed Limit signs. It was found that the accuracy of recognition was 92.75% which indicates clearly the high robustness targeted by this system. Published 2017-01-04T05:16:16Z 2017-01-04T05:16:16Z 2014 Conference paper Biswas, R., Tora, M. R., & Bhuiyan, F. H. (2014). LVQ and HOG based speed limit traffic signs detection and categorization. Paper presented at the 2014 International Conference on Informatics, Electronics and Vision, ICIEV 2014, doi:10.1109/ICIEV.2014.6850741 978-147995179-6 http://hdl.handle.net/10361/7508 http://dx.doi.org/10.1109/ICIEV.2014.6850741 en http://ieeexplore.ieee.org/document/6850741/ © 2014 IEEE Computer Society |
| institution |
Brac University |
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Institutional Repository |
| language |
English |
| topic |
Circular hough transform HOG LVQ SVM Traffic sign |
| spellingShingle |
Circular hough transform HOG LVQ SVM Traffic sign Biswas, Rubel Tora, Moumita Roy Bhuiyan, Farazul Haque LVQ and HOG based speed limit traffic signs detection and categorization |
| description |
This conference paper was presented in the International Conference on Informatics, Electronics and Vision, ICIEV 2014; Dhaka; Bangladesh; 23 May 2014 through 24 May 2014 [© 2014 IEEE] The conference paper's definite version is available at: http://dx.doi.org/10.1109/ICIEV.2014.6850741 |
| author2 |
Department of Computer Science and Engineering, BRAC University |
| author_facet |
Department of Computer Science and Engineering, BRAC University Biswas, Rubel Tora, Moumita Roy Bhuiyan, Farazul Haque |
| format |
Conference paper |
| author |
Biswas, Rubel Tora, Moumita Roy Bhuiyan, Farazul Haque |
| author_sort |
Biswas, Rubel |
| title |
LVQ and HOG based speed limit traffic signs detection and categorization |
| title_short |
LVQ and HOG based speed limit traffic signs detection and categorization |
| title_full |
LVQ and HOG based speed limit traffic signs detection and categorization |
| title_fullStr |
LVQ and HOG based speed limit traffic signs detection and categorization |
| title_full_unstemmed |
LVQ and HOG based speed limit traffic signs detection and categorization |
| title_sort |
lvq and hog based speed limit traffic signs detection and categorization |
| publisher |
© 2014 IEEE Computer Society |
| publishDate |
2017 |
| url |
http://hdl.handle.net/10361/7508 http://dx.doi.org/10.1109/ICIEV.2014.6850741 |
| work_keys_str_mv |
AT biswasrubel lvqandhogbasedspeedlimittrafficsignsdetectionandcategorization AT toramoumitaroy lvqandhogbasedspeedlimittrafficsignsdetectionandcategorization AT bhuiyanfarazulhaque lvqandhogbasedspeedlimittrafficsignsdetectionandcategorization |
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1814308523810488320 |