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Title: A Time Efficient Model for Region of Interest Extraction in Real Time Traffic Signs Recognition System
Authors: Qararyah, Fareed
Daraghmi, Yousef-Awwad
Daraghmi, Eman
Rajora, Shantanu
Lin, Chin-Teng
Prasad, Mukesh
Keywords: Image color analysis , Image segmentation , Mathematical model , Computational modeling , Lighting , Logistics , Real-time systems
Issue Date: 2019
Publisher: IEEE
Citation: F. Qararyah, Y. Daraghmi, E. Daraghmi, S. Rajora, C. Lin and M. Prasad, "A Time Efficient Model for Region of Interest Extraction in Real Time Traffic Signs Recognition System," 2018 IEEE Symposium Series on Computational Intelligence (SSCI), Bangalore, India, 2018, pp. 83-87. doi: 10.1109/SSCI.2018.8628874 keywords: {feature extraction;image classification;image colour analysis;image segmentation;object detection;regression analysis;road safety;traffic engineering computing;robust segmentation methods;trained segmentation classifier;feature extraction;logistic regression;image color segmentation;real time traffic sign recognition system;region of interest extraction;traffic sign images;detection phase;TSR systems;vehicle safety;intelligent vehicles;computation intelligence;RGB color space;time efficient color segmentation model;traffic sign detection;Image color analysis;Image segmentation;Mathematical model;Computational modeling;Lighting;Logistics;Real-time systems;Traffic sign recognition;traffic sign detection;Logistic Regression;computational time}, URL:
Abstract: Computation intelligence plays a major role in developing intelligent vehicles, which contains a Traffic Sign Recognition (TSR) system for increasing vehicle safety. Traffic sign recognition systems consist of an initial phase called Traffic Sign Detection (TSD), where images and colors are segmented and fed to the recognition phase. The most challenging process in TSR systems in terms of time consumption is the detection phase. The previous studies proposed different models for traffic sign detection, however, the computation time of these models still requires improvement for enabling real time systems. Therefore, this paper focuses on the computational time and proposes a novel time efficient color segmentation model based on logistic regression. This paper uses RGB color space as the domain to extract the features of our hypothesis; this has boosted the speed of the proposed model, since no color conversion is needed. The trained segmentation classifier is tested on 1000 traffic sign images taken in different lighting conditions. The experimental results show that the proposed model segmented 974 of these images correctly and in a time less than one-fifth of the time needed by any other robust segmentation methods.
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