Vehicle Classification Using Visual Background Extractor and Multi-class Support Vector Machines

被引：12

作者：

Ng, Lee Teng ^{[1
]}

Suandi, Shahrel Azmin ^{[1
]}

Teoh, Soo Siang ^{[1
]}

机构：

[1] Univ Sains Malaysia, Intelligent Biometr Grp, Sch Elect & Elect Engn, Nibong Tebal 14300, Pulau Pinang, Malaysia

来源：

8TH INTERNATIONAL CONFERENCE ON ROBOTIC, VISION, SIGNAL PROCESSING & POWER APPLICATIONS: INNOVATION EXCELLENCE TOWARDS HUMANISTIC TECHNOLOGY | 2014年 / 291卷

关键词：

Vehicle classification; Visual background extractor; Support vector machines; Histogram of oriented gradient;

D O I：

10.1007/978-981-4585-42-2_26

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：

This paper describes a method to classify vehicle type using computer vision technology. In this study, Visual Background Extractor (ViBe) was used to extract the vehicles from the captured videos. The features of the detected vehicles were extracted using Histogram of Oriented Gradient (HOG). Multi-class Support Vector Machine (SVM) was used to recognise four classes of images: motorcycle, car, lorry and background (without vehicles). The results show that the proposed classifier was able to achieve an average accuracy of 92.3 %.

引用

页码：221 / 227

页数：7

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