PERFORMANCE IMPROVEMENT IN TRAFFIC VISION SYSTEMS USING SVMS

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.