scispace - formally typeset
Proceedings ArticleDOI

Neural network based smart vision system for driver assistance in extracting traffic signposts

Reads0
Chats0
TLDR
The methodology used to detect the road sign from a traffic sign scene image is discussed and a technique that can be used to construct a system that recognizes road signs in images is presented.
Abstract
A vision-based driver assistance system using image processing technology consists of three main modules: road detection, traffic sign detection/identification, obstacle detection. Traffic Sign Recognition is an upcoming research field under applied Computer Vision and Machine Learning which deals with the automatic detection and classification of traffic signs in traffic scene images. In the current paper, we discuss the methodology used to detect the road sign from a traffic sign scene image and present a technique that can be used to construct a system that recognizes road signs in images. Such a system will ensure that each driver is aware of the rules & hazards on the road & will thus help in improving road safety. Also, the location of the road sign in the traffic sign image is unknown. Once these obstacles are overcome, such a system could be integrated in a Driver Assistance System. The primary objective is to develop an algorithm, which will identify various types of road signs from streaming video of real time road scenes in a reasonable time frame. The algorithm has two main parts: the first one, detection which uses the color and shape information to detect a road sign in a traffic scene picture. The second one, prediction & classification based on Neural Network concept. Our algorithm proposes a variety of MATLAB Image Processing Toolbox commands to determine if a road sign is present in current image. If present, the sign is resized and certain features from the Region of Interest (RoI) are fed as a feature vector to a neural net. This trained Neural Classifier then predicts the class of the road sign. Genetic Algorithms (GA) with built-in intelligence could be incorporated into the current system which will try to look for traffic signs only in environments where there is probability of finding/detecting one.

read more

Citations
More filters
Proceedings ArticleDOI

Traffic Signs Detection and Recognition System using Deep Learning

TL;DR: In this article, an approach for efficiently detecting and recognizing traffic signs in real-time, taking into account the various weather, illumination and visibility challenges through the means of transfer learning is described.
References
More filters
Journal ArticleDOI

Road-sign detection and tracking

TL;DR: The experimental results demonstrate that the proposed method performs well in both detecting and tracking road signs present in complex scenes and in various weather and illumination conditions.
Journal ArticleDOI

Robust method for road sign detection and recognition

TL;DR: The proposed approach can be very helpful for the development of a system for driving assistance and is robust against low-level noise corrupting edge detection and contour following, and works for images of cluttered urban streets as well as country roads and highways.
Journal ArticleDOI

Real-time traffic sign recognition from video by class-specific discriminative features

TL;DR: An efficient road sign recognition system is built, based on a conventional nearest neighbour classifier and a simple temporal integration scheme, which demonstrates a competitive performance in the experiments involving real traffic video.
Journal ArticleDOI

Road sign detection and recognition using matching pursuit method

TL;DR: This paper describes an automatic road sign recognition system by using matching pursuit (MP) filters that finds the relative position of road sign in the original distant image by using a priori knowledge, shape and color information and captures a closer view image.
Journal ArticleDOI

Image Segmentation and Shape Analysis for Road-Sign Detection

TL;DR: Experimental results on real-life images show a high success rate and a very low false hit rate and demonstrate that the proposed framework is invariant to translation, rotation, scale, and partial occlusions.