Earticle

초록

영어

Automatic traffic scene analysis which has been used for real-time on-road vehicle detection system is essential to many areas of ITS (Intelligent Transport Systems). In order to improve the detection time and accuracy of detection performance, various image processing techniques have been used for real-time vehicle detection. Moreover, Neural Networks have been increasingly and successfully applied to many problems for ITS research topics. Support Vector Machines (SVMs) are currently another efficient approach to vehicle detection because of their remarkable performance. In this research, two different models, Backpropagation which is the best-known neural network model and SVMs have been studied to compare their performance in predictive accuracy, through experiment with real world image data of traffic scenes. Experimental results show that SVMs can provide higher performance in terms of predictive performance than the well-known Backpropagation neural network model.

목차

Abstract
 1. Introduction
 2. Backpropagation and SVMs (Support Vector Machines)
  3.1. Backpropagation Neural Networks
  3.2. Support Vector Machines
 3. Experiments and results
  3.1. Data Sets for Learning and Testing
  3.2. Network Architecture and Parameter Value
  3.3. Predictive Performance of Backpropagation and SVMs
 4. Conclusion
 Acknowledgements
 References

키워드

저자

• Heejong Suh [ Department of Electronic Communication Engineering, Chonnam National University ]
• Daehyon Kim [ Department of Marine and Civil Engineering, Chonnam National University ] Corresponding author
• Changsoo Jang [ Department of Computer Engineering, Chonnam National University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Science and Technology

• 간기

  월간

• pISSN

  2005-4238

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605