Vehicle Classification Using Visual Background Extractor and Multi-class Support Vector Machines
Vehicle Classification Using Visual Background Extractor and Multi-class Support Vector Machines
- Neue Suche nach: Ng, Lee Teng
- Neue Suche nach: Suandi, Shahrel Azmin
- Neue Suche nach: Teoh, Soo Siang
- Neue Suche nach: Mat Sakim, Harsa Amylia
- Neue Suche nach: Mustaffa, Mohd Tafir
- Neue Suche nach: Ng, Lee Teng
- Neue Suche nach: Suandi, Shahrel Azmin
- Neue Suche nach: Teoh, Soo Siang
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 %.
Dokumentinformationen
Vehicle Classification Using Visual Background Extractor and Multi-class Support Vector Machines
Lect. Notes Electrical Eng.
The 8th International Conference on Robotic, Vision, Signal Processing & Power Applications ; Kapitel : 26 ; 221-227
2014-02-27
7 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Vehicle classification , Visual background extractor , Support vector machines , Histogram of oriented gradient Engineering , Control, Robotics, Mechatronics , Artificial Intelligence , Signal, Image and Speech Processing , Communications Engineering, Networks , Power Electronics, Electrical Machines and Networks
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