Automatic road sign detecion and classification based on support vector machines and HOG descriptos

Abstract

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.

Road Signs Detection and Recognition Utilizing Images and 3d Point Cloud Acquired by Mobile Mapping System

“…Then in the classification stage, the detected road sign candidates are assigned to the class that they belong. For the detection stage, there are color based detection (Lopez and Fuentes, 2006), shape based detection (Loy and Barnes, 2004) and the combination of the two approaches (Adam and Ioannidis, 2014). Detection stage is very important because the road signs that are not detected during this step is not available to be recovered later.…”

“…Detection stage is very important because the road signs that are not detected during this step is not available to be recovered later. Classification shall be conducted using traditional template matching (Siogkas and Dermatas, 2006) or via techniques from the field of machine learning, such as Support Vector Machines (Maldonado-Bascon et al, 2007;Adam and Ioannidis, 2014) and deep learning (Sermanet and LeCun, 2012;Ciresan et al, 2012). Especially, Ciresan et al, (2012) presented a state-of-the-art road sign classification approach using deep neural network, which won the German * Corresponding author traffic sign recognition benchmark with a recognition rate of 99.46%, better than the one of humans on this task.…”

Circular Traffic Sign Classification Using Hog Based Ring Partitioned Matching

“…The classifier-based methods use the machine learning classifiers, such as the Support Vector Machine (SVM) [3][4][5][6][7][8][9][10], the random forest [11], the Artificial Neural Network (ANN) [12][13][14], [18], [19], and the AdaBoost algorithm [15]. The template-based methods use the cross-correlation algorithm [16] and the histogram matching [17].…”

“…The aim of DAS is to provide a safety driving to the driver, such as to monitor the driver fatigue [1], detect the road lane [2], and recognize the traffic signs [3][4][5][6][7][8][9][10][11][12][13][14][15][16]. Traffic sign recognition (TSR) system is one of the popular and challenging topics in the DAS.…”

“…Traffic sign recognition (TSR) system is one of the popular and challenging topics in the DAS. Basically, TSR consists of two steps [3][4][5][6][7][8][9][10][11][12][13][14][15][16]: traffic sign detection and classification. Traffic sign detection is used to detect the appearance of the traffic sign on an image.…”