International Journal of Engineering Research in Electronics and Communication Engineering(IJERECE)

Monthly Journal for Electronics and Communication Engineering

Volume4,August 2017,

Topic : Efficient Approach for Traffic Sign Detection based on Saliency Detection via Graph-Based Manifold Ranking

Authors:Miss.Namrata Vijay Chavan || Prof.M.S. Borse

Abstract:Now days, traffic sign detection attracted large number of researchers interest due to its important in efficient intelligent transportation systems (ITSs). Traffic sign detection helps to minimize the road accidents and hence to minimize the loss. The efficient traffic sign detection is vital research problem since from last decade. In ITSs, there is more work conducted already on name plate detection and recognition, however there only few concrete research studies presented to solve the problem of traffic sign detection and recognition. For applications like road surveying, autonomous vehicles are mainly demanding the system of road side sign detection and recognition. The current methods for road side sign detection having issues of efficiency and accuracy due to different factors affecting on road sign detection such as shadow, non-uniform sizes of signs, illumination conditions, blurring, occlusion, and sign deterioration etc. In this work, we proposed novel method for road sign detection and recognition based on saliency regions detection. Saliency regions detection helps to locate the road sign efficiently and hence traffic sign detection properly. In this paper, author introduced the efficient segmentation method and graph based ranking approach for the accurate detection salient regions. Additionally we applied the RGB image smoothing algorithm to improve the detection accuracy. The performance results claims that proposed approach outperforming the previous method..

Keywords: Saliency Detection, Traffic Sign Detection, Segmentation, Salient regions, Manifold ranking

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DOI: 01.1617/vol4/iss8/pid49265


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Editor-in-Chief

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Dr.Zhongfu Tan
National Professor,
North China Electric Power University,
CHINA


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ISSN(Online): 2394-6849

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Thomson Reuters ID : q-6288-2016.
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