Warning traffic sign detection using learning vector quantization & hough transform and recognition based on HOG
This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2014.
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| Формат: | Дисертація |
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BRAC University
2014
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| Онлайн доступ: | http://hdl.handle.net/10361/2941 |
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10361-29412022-01-26T10:18:16Z Warning traffic sign detection using learning vector quantization & hough transform and recognition based on HOG Tora, Moumita Roy Biswas, Rubel Department of Computer Science and Engineering, BRAC University Computer science and engineering This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2014. Cataloged from PDF version of thesis report. Includes bibliographical references (page 51). Traffic Sign Recognition (TSR) is used to regulate traffic signs, warn a driver, and command or prohibit certain actions. Fast real-time and robust automatic traffic sign detection and recognition can support and disburden the driver and significantly increase driving safety and comfort. Automatic recognition of traffic signs is also important for an automated intelligent driving vehicle or for driver assistance systems. This paper aims to present a color segmentation approach for traffic sign recognition based on LVQ neural network and also focuses on triangular edge detection and feature extraction based on Hough transformation and HOG respectively. At first samples of images in different weather conditions are collected and then RGB images are converted into HSV color space. The samples are then trained using LVQ depending on the hue and saturation values of each pixel and then tested for color segmentation. The edges of the triangular segmented images are then detected using Hough Transformation. Then samples are taken to extract features using HOG. Finally they are trained and tested using SVM to get the output image. The algorithms were applied to around 100 sampled images taken in different countries and varied weather conditions. Despite the varying conditions, the algorithms worked almost accurately in all situations and the success rate was quite satisfactory with a very good response time of a few milliseconds. B. Computer Science and Engineering 2014-02-17T07:09:09Z 2014-02-17T07:09:09Z 2014 2014-01 Thesis ID 11301025 http://hdl.handle.net/10361/2941 en BRAC University thesis are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 51 pages application/pdf BRAC University |
| institution |
Brac University |
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Institutional Repository |
| language |
English |
| topic |
Computer science and engineering |
| spellingShingle |
Computer science and engineering Tora, Moumita Roy Warning traffic sign detection using learning vector quantization & hough transform and recognition based on HOG |
| description |
This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2014. |
| author2 |
Biswas, Rubel |
| author_facet |
Biswas, Rubel Tora, Moumita Roy |
| format |
Thesis |
| author |
Tora, Moumita Roy |
| author_sort |
Tora, Moumita Roy |
| title |
Warning traffic sign detection using learning vector quantization & hough transform and recognition based on HOG |
| title_short |
Warning traffic sign detection using learning vector quantization & hough transform and recognition based on HOG |
| title_full |
Warning traffic sign detection using learning vector quantization & hough transform and recognition based on HOG |
| title_fullStr |
Warning traffic sign detection using learning vector quantization & hough transform and recognition based on HOG |
| title_full_unstemmed |
Warning traffic sign detection using learning vector quantization & hough transform and recognition based on HOG |
| title_sort |
warning traffic sign detection using learning vector quantization & hough transform and recognition based on hog |
| publisher |
BRAC University |
| publishDate |
2014 |
| url |
http://hdl.handle.net/10361/2941 |
| work_keys_str_mv |
AT toramoumitaroy warningtrafficsigndetectionusinglearningvectorquantizationhoughtransformandrecognitionbasedonhog |
| _version_ |
1814308777992650752 |