Feature selection using Ant Colony Optimization (ACO) and Road Sign Detection and Recognition (RSDR) system |
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| Affiliation: | 1. Department of Information Technology, Christian College of Engineering and Technology, Oddanchatram 6246192, India;2. Christian College of Engineering and Technology, Oddanchatram 6246192, India;1. Department of Surgery, Swedish American Health System, Rockford, Illinois;2. Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, New York;1. Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran;2. School of Computing, Science and Engineering, University of Salford, Manchester, United Kingdom;1. Natural Language Processing (NLP) Research Lab, Faculty of Electrical and Computer Engineering, Shahid Beheshti University, G.C, Tehran, Iran;2. Department of Computing, Asia Pacific University, Kuala Lumpur, Malaysia;3. Department of Computer Science, Aberystwyth University, Ceredigion, Wales, UK;4. Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran;2. Sapporo Cardio Vascular Center, Sapporo, Hokkaido, Japan |
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| Abstract: | Road Sign Detection and Recognition (RSDR) is aimed to enable drivers maintain basic functionality with the aim of identifying and notifying driver through the existing restrictions so that the process is a success on the present widened road. Examples for RSDR include ‘traffic light ahead’ or ‘pedestrian crossing’ signs. An innovative RSDR system has been introduced which comprises of pre-processing, edge detection, feature extraction, features selection and Ensemble Fuzzy Support Vector Machine (EFSVM) classifier. Feature selection is carried out successfully by deployment of Ant Colony Optimization (ACO) algorithm to determine most prominent and definitive features. These features are then fed into the ensemble SVM to enable both road side traffic detection as well as recognition. Suggested system’s performance is analyzed and evaluated with respect to road signs having a capable recognition rate. |
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| Keywords: | Ant Colony Optimization (ACO) Road Sign Detection and Recognition (RSDR) Feature selection, classification and Ensemble Fuzzy Support Vector Machine (EFSVM) |
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