Driving behavior analysis based on support vector machines for visual traffic surveillance

With the rapid development of our economy, the number of automobiles is growing for 8% in every year, which brings people's convenience, at the same time, which brings a lot of traffic problems. Traffic accident happened frequently, and traffic carry capacity became insufficient. Statistics result showed that one main reason was caused by driving behavior mistakes. In this paper, we will present a real-time method that can detect, track and analyze the driving vehicles' behaviors. The proposed method contains three parts, which are vehicle detection, vehicle tracking and driving behavior analysis. Each part's algorithms were filtered and were improved separately, and I got the typical driving behavior sample through hand-drawing vehicle's travel path, and use Support Vector Machine (SVM) for machine learning Experimental results showed that it can accurately judge monitored vehicle's driving behavior, and realize efficient surveillance for vehicles' the driving behavior who was in violation.