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Streetlight Objects Recognition by Region and Histogram Features in an Autonomous Vehicle System


Affiliations
1 Department of Computer Science, Nigerian Defence Academy, Nigeria
     

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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.

Keywords

Streetlight Recognition, Autonomous Vehicles, Image Histogram Features, Region Features, Support Vector Machine.
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  • Streetlight Objects Recognition by Region and Histogram Features in an Autonomous Vehicle System

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Authors

Martins E. Irhebhude
Department of Computer Science, Nigerian Defence Academy, Nigeria
Michael Shabi
Department of Computer Science, Nigerian Defence Academy, Nigeria
Adeola Kolawole
Department of Computer Science, Nigerian Defence Academy, Nigeria

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.

Keywords


Streetlight Recognition, Autonomous Vehicles, Image Histogram Features, Region Features, Support Vector Machine.

References