Abstract
Computer vision system is one of important research topics in ITS(Intelligent Transport Systems). Moreover, Neural Networks have been increasingly and successfully applied to many problems for ITS. Even though there are currently many different types of neural network models, Backpropagation is the most popular neural network model. It is however known that the Support Vector Machines (SVMs) based on the statistical learning theory is currently another efficient approach for pattern recognition problem since their remarkable performance in terms of prediction accuracy. In this research, two different models, Backpropagation and SVMs have been studied to compare their performance in predictive accuracy through the experiment with real world image data of traffic scenes. Experimental results show that SVMs can provide higher performance in terms of prediction performance than any other models.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of the Eastern Asia Society for Transportation Studies
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
