ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-5, 1-7, 2014
https://doi.org/10.5194/isprsannals-II-5-1-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
28 May 2014
Automatic road sign detecion and classification based on support vector machines and HOG descriptos
A. Adam and C. Ioannidis NTUA, School of Rural & Surveying Engineering, Athens 15780, Greece
Keywords: Detection, Classification, Colour, Automation, Vision, Learning Abstract. This paper examines the detection and classification of road signs in color-images acquired by a low cost camera mounted on a moving vehicle. A new method for the detection and classification of road signs is proposed based on color based detection, in order to locate regions of interest. Then, a circular Hough transform is applied to complete detection taking advantage of the shape properties of the road signs. The regions of interest are finally represented using HOG descriptors and are fed into trained Support Vector Machines (SVMs) in order to be recognized. For the training procedure, a database with several training examples depicting Greek road sings has been developed. Many experiments have been conducted and are presented, to measure the efficiency of the proposed methodology especially under adverse weather conditions and poor illumination. For the experiments training datasets consisting of different number of examples were used and the results are presented, along with some possible extensions of this work.
Conference paper (PDF, 541 KB)


Citation: Adam, A. and Ioannidis, C.: Automatic road sign detecion and classification based on support vector machines and HOG descriptos, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-5, 1-7, https://doi.org/10.5194/isprsannals-II-5-1-2014, 2014.

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