International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 04 | Apr -2017
p-ISSN: 2395-0072
www.irjet.net
Traffic Sign Detection and Recognition Using Open CV Prachi Gawande1 1Asstt.
Professor, Dept of Electronics &Telecommunication Engg, YCCE, Maharashtra, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - This paper reviews the method for traffic sign
detection and classification of complex objects with a lot of intra-class variation, such as bicycles, aero planes, chairs or animals. Contemporary detection and classification algorithms will perform really well in detecting and classifying a traffic sign in an image. However, as research comes closer to commercial applications, the constraints of the problem change. In driver assistance systems or road inventory systems, the problem is no longer how to efficiently detect and recognize a traffic sign in a single image, but how to reliably detect it in hundreds of thousands of video frames without any false alarms, often using lowquality cheap sensors available in mass production. To illustrate the problem of false alarms, consider the following: one hour of video shot at 24 frames per second consists of 86400 frames. If we assume that in the video under consideration traffic signs appear every three minutes and typically span through 40 frames, there are a total of 800 frames which contain traffic signs and 85600 frames which do not contain any signs. These 85600 frames without traffic
detection and recognition. In the section on learning-based detection, we review the Viola Jones detector and the possibility of applying it to traffic sign detection. The recognition of the detected traffic sign is handled by the Histogram of Gradient based SVM classifier. Together this system is expected to perform much better than the other systems available. The algorithms when trained with proper set of images have been noted to perform accurately. This must hold true for the traffic signs as well under different color, lighting, atmospheric conditions.
Key Words: OpenCV, Haar features, Cascades classification, Machine Learning, Histogram of Gradient, Cascade Training, SVM, KNN, Feature matching.
1.
INTRODUCTION
In recent years there is increase in computing power have brought computer vision to consumer-grade applications. As computers offer more and more processing power, the goal of real-time traffic sign detection and recognition is becoming feasible. Some new models of high class vehicles already come equipped with driver assistance systems which offer automated detection and recognition of certain classes of traffic signs. Traffic sign detection and recognition is also becoming interesting in automated road maintenance. Traffic symbols have several distinguishing features that may be used for their detection and identification. They are designed in specific colours and shapes, with the text or symbol in high contrast to the background. Every road has to be periodically checked for any missing or damaged signs; as such signs pose safety threats. The checks are usually done by driving a car down the road of interest and recording any observed problem by hand. The task of manually checking the state of every traffic sign is long, tedious and prone to human error. By using techniques of computer vision, the task could be automated and therefore carried out more frequently, resulting in greater road safety. To a person acquainted with recent advances in computer vision, the problem of traffic sign detection and recognition might seem easy to solve. Traffic signs are fairly simple objects with heavily constrained appearances. Just a glance at the wellknown PASCAL visual object classes challenge for 2009 indicates that researchers are now solving the problem of
Š 2017, IRJET
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Impact Factor value: 5.181
Fig 1. Some examples of Traffic Signs on road. signs will be presented to our detection system. If our system were to make an error of 1 false positive per 10
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