LVQ and HOG based speed limit traffic signs detection and categorization

Abstract

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.