{"title":"STREETLIGHT OBJECTS RECOGNITION BY REGION AND HISTOGRAM FEATURES IN AN AUTONOMOUS VEHICLE SYSTEM","authors":"Martins E. Irhebhude, M. Shabi, A. Kolawole","doi":"10.21917/ijivp.2019.0292","DOIUrl":null,"url":null,"abstract":"In this paper Streetlight object identification is addressed using the notion of image processing. An approach based on Image Processing Techniques is proposed for selection and processing of features from the images. Histogram and Region was applied on the extracted images. Histogram and Region features were then extracted and employed to train the Support Vector Machine (SVM) classifier for streetlight recognition. Experimental results shows 99.1%, 84% and 100% for histogram, region features and combination of both respectively. Experimental results have proved that the proposed method is robust, accurate, and powerful in object recognition.","PeriodicalId":30615,"journal":{"name":"ICTACT Journal on Image and Video Processing","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ICTACT Journal on Image and Video Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21917/ijivp.2019.0292","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In this paper Streetlight object identification is addressed using the notion of image processing. An approach based on Image Processing Techniques is proposed for selection and processing of features from the images. Histogram and Region was applied on the extracted images. Histogram and Region features were then extracted and employed to train the Support Vector Machine (SVM) classifier for streetlight recognition. Experimental results shows 99.1%, 84% and 100% for histogram, region features and combination of both respectively. Experimental results have proved that the proposed method is robust, accurate, and powerful in object recognition.
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