International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1398 TRAFFIC SIGN AND DROWSINESS DETECTION USING OPEN-CV R. Prem Kumar 1 , M. Sangeeth 2 , K.S. Vaidhyanathan 3 , Mr. A. Pandian 4 1,2,3 Student, Department of ECE, SRM Valliammai Engineering College, Tamilnadu, India. 4 Assistant professor, Department of ECE, SRM Valliammai Engineering College, Tamilnadu, India. ----------------------------------------------------------------------***--------------------------------------------------------------------- ABSTRACT - The detection of traffic sign from images plays a vital role in Computer vision .The various machine and mathematical models, for classifying traffic sign including Scale Invariant Feature transform (SIFT) and Dominant Rotated Local Binary Pattern (DRLBP) has been proposed yields better performance. This paper proposes a novel method of classifying the traffic sign using Artificial Neural Network. This is done by pre-processing the traffic sign image at first and then extracting the face features using SIFT. Then the detection of traffic sign is done using Back Propagation Network (BPN). The processes of combining SIFT and DRLBP perform better rather using separately. In addition, this paper is to develop a technique for drowsiness detection system. Our whole focus and concentration will be placed on designing the system that will accurately monitor the open and closed state of the person’s eye. By constantly monitoring the eyes, it can be seen that the symptoms of person fatigue can be detected early enough to avoid an accident. This detection will be done employing a sequence of pictures of eyes further as face.The observation of eye movements and its edges for the detection are used KEYWORDS - OPEN-CV, SIFT, DRLBP, GLCM, ANN-Back Propagation Network, HAAR CASCADE INTRODUCTION Road safety is always an area that concerned many people around the world and systems that aid the drivers have been appearing ever since cars and computers were combined to make driving safer and more efficient. There are plenty of systems that are able to warn drivers about different types of dangers: lane departure, collision possibility and various traffic signs. However, there is still room for development, because modern technologies, like the rising vision about the OPEN-CV, allow us to create much more efficient systems. Also, the detections can be improved to perform better in various situations, such as different light conditions, road quality, etc. In this project, we present the plans of a driver-assistance system, which will be capable of road lane and traffic sign detection by using an OPEN-CV. LITERATURE SURVEY In earlier studies for drowsiness detection, few works has been found that the algorithm recognized total face and consumed more time[9] .These algorithm recognized only static image of the face and stored in database[7]. Eye detection was recognized only in few algorithm through EEG signals which was fast in reaction but accuracy was less[2]. Later dynamic image was captured for more accuracy but only drawback was framing the eye. EEG based detection resulted in less robust system, longer computational time and restricted in use in series neural network[5]. When it was taken for real time implementation it cannot predict it easily and normal state will be considered for the yawning. For traffic sign detection appearance based methods involves LDA and Geometric methods[3]. In geometric based methods, the geometric features considered which does not provided optimal results[6]. III. MATERIALS AND METHODS This section provides detail view on participants Involved, drowsiness detection and thepreprocessing methodology followed to detect traffic sign. FLOW CHART TRAFFIC SIGN DETECTION