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http://dx.doi.org/10.13067/JKIECS.2013.8.8.1129

Vehicle Detection Using Optimal Features for Adaboost  

Kim, Gyu-Yeong ((주)에이치엠씨 부설연구소)
Lee, Geun-Hoo (경성대학교 전자공학과)
Kim, Jae-Ho (부산대학교 전자공학과)
Park, Jang-Sik (경성대학교 전자공학과)
Publication Information
The Journal of the Korea institute of electronic communication sciences / v.8, no.8, 2013 , pp. 1129-1135 More about this Journal
Abstract
A new vehicle detection algorithm based on the multiple optimal Adaboost classifiers with optimal feature selection is proposed. It consists of two major modules: 1) Theoretical DDISF(Distance Dependent Image Scaling Factor) based image scaling by site modeling of the installed cameras. and 2) optimal features selection by Haar-like feature analysis depending on the distance of the vehicles. The experimental results of the proposed algorithm shows improved recognition rate compare to the previous methods for vehicles and non-vehicles. The proposed algorithm shows about 96.43% detection rate and about 3.77% false alarm rate. These are 3.69% and 1.28% improvement compared to the standard Adaboost algorithmt.
Keywords
Vehicle Detection; Adaboost Algorithm; Haar-Like Feature; Site Model;
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Times Cited By KSCI : 3  (Citation Analysis)
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