An efficient selection of HOG feature for SVM classification of vehicle

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 Ass...

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Bibliographic Details
Published in2015 International Symposium on Consumer Electronics (ISCE) pp. 1 - 2
Main Authors Seung-Hyun Lee, MinSuk Bang, Kyeong-Hoon Jung, Kang Yi
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2015
Subjects
Accuracy
ADAS
Advanced driver assistance systems
Feature extraction
Histograms
HOG
Support vector machines
SVM
Vehicle detection
Vehicles
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Summary: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.
ISSN:0747-668X
2159-1423
DOI:10.1109/ISCE.2015.7177766