An Efficient Selection of HOG Feature for SVM Classification of Vehicle

被引：0

作者：

Lee, Seung-Hyun ^{[1
]}

Bang, MinSuk ^{[1
]}

Jung, Kyeong-Hoon ^{[1
]}

Yi, Kang ^{[2
]}

机构：

[1] Kookmin Univ, Sch Elect Engn, Seoul, South Korea

[2] Handong Global Univ, Sch Comp Sci & Elect Engn, Pohang, South Korea

来源：

2015 IEEE INTERNATIONAL SYMPOSIUM ON CONSUMER ELECTRONICS (ISCE) | 2015年

关键词：

ADAS; vehicle detection; HOG; SVM; HISTOGRAMS; ROAD;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Support Vector Machine (SVM) classifier with Histogram of Oriented Gradients (HOG) feature become one of the most popular techniques used for vehicle detection in recent years. And the computing time of SVM is a main obstacle to get real time implementation which is important for Advanced Driver Assistance Systems (ADAS) applications. One of the effective ways to reduce the computing complexity of SVM is to reduce the dimension of HOG feature. In this paper, we examine the effect of the number of HOG bins on the vehicle detection and the symmetric characteristics of HOG feature of vehicle. And we successfully demonstrate the speed-up of SVM classifier for vehicle detection by about three times while maintaining the detection performance.

引用

页数：2

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