Pregled bibliografske jedinice broj: 1015874
The Speed Limit Road Signs Recognition Using Hough Transformation and Multi-Class Svm
The Speed Limit Road Signs Recognition Using Hough Transformation and Multi-Class Svm // PROCEEDINGS OF IWSSIP 2019 / Žagar, Drago ; Rimac-Drlje, Snježana ; Martinović, Goran ; Galić, Irena ; Vranješ, Denis ; Habijan, Marija (ur.).
Osijek: Institute of Electrical and Electronics Engineers (IEEE), 2019. str. 89-94 doi:10.1109/iwssip.2019.8787249 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
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Naslov
The Speed Limit Road Signs Recognition Using Hough Transformation and Multi-Class Svm
Autori
Matoš, Ivona ; Krpić, Zdravko ; Romić, Krešimir
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
PROCEEDINGS OF IWSSIP 2019
/ Žagar, Drago ; Rimac-Drlje, Snježana ; Martinović, Goran ; Galić, Irena ; Vranješ, Denis ; Habijan, Marija - Osijek : Institute of Electrical and Electronics Engineers (IEEE), 2019, 89-94
ISBN
978-1-7281-3227-3
Skup
26th International Conference on Systems, Signals and Image Processing (IWSSIP 2019)
Mjesto i datum
Osijek, Hrvatska, 05.06.2019. - 07.06.2019
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
speed limit traffic signs recognition, Hough Transformation, Histogram of Oriented Gradients, Support Vector Machines
Sažetak
In this paper, a method for the speed limit traffic sign recognition is proposed. The method is based on Support Vector Machines, which is one of the most efficient algorithms used for traffic sign recognition. It comprises three phases. In the preprocessing phase, RGB images are converted into HSL images in order to increase the contrast. In the detection phase, Hough Transformation is used for detecting the speed limit signs along with Gauss and Median filters for removing the noise from the detected images. The detection phase achieves accuracy of 95.3%. In the classification phase, a Histogram of Oriented Gradients descriptor for feature extraction is used together with Support Vector Machines for image classification and speed limit sign recognition. The proposed method was used on the two databases - GTSRB, German Traffic Sign Recognition Benchmark and rMASTIF, Croatian traffic sign database. The recognition accuracy of 93.75% is achieved. The presented method proves to be applicable in advancing driving assistance systems due to its detection and recognition accuracy as well as its performance, thus making it appropriate for real-time applications.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo, Informacijske i komunikacijske znanosti
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek