A novel method for the extraction of primary visual features from an image through intelligent feature descriptors

Abstract

As far as the safety of a driver is concerned, more focus should be put on correct interpretation and information which is conveyed by a traffic sign, while driving a vehicle along the road. A sign board can be thought of as an emblem which disseminates important and meaningful information regarding the potential hazards prevailing among road users comprising roadways cladded with snowfall, construction worksites or repairing of roads taking place and telling the people to follow an alternative route. It alerts the person who is passing through the road about the maximum possible extremity that his vehicle is trying to achieve indicating slowing down the speed of vehicle since chances of having collision cannot be ruled out. With constant increasing of the training database size, not only there cognition accuracy, but also the computation complexity should be considered in designing a feasible recognition approach. The traffic sign images were acquired from the image database and were subjected to some pre-processing techniques such as conversion of the original RGB images into HSV Color Space, Adjustment of the Contrast of the Color images as well as applying the Histogram of Oriented Gradients (HOG) algorithm in which the process of extraction and plotting of the HOG features from a given image is performed that is most popular amongst the feature extraction algorithms. In the future, we will concentrate on detecting, recognizing as well as classifying a particular sign board.