Warning traffic sign detection using learning vector quantization & hough transform and recognition based on HOG

Abstract

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.