Vehicle detection for intelligent traffic surveillance system

Abstract

Due to the dramatical grow of road transport, Advanced Driver Assistance Systems (ADAS) are being one of the most popular system. The main challenge of these systems is to improve driving safety and reduce accidents. Robust and effective vehicle detection is a critical step. However, vehicle detection meets many difficulties such as complex background, different size, model and orientations of vehicle. To solve this problem, this paper introduces an approach for traffic vehicle detection based on multi-scale covariance descriptor (MSCOV) for the image description and support vector machine classifier (SVM) for the data classification. This method is evaluated and compared to existing detection approach. The result of this approach outperforms existing vehicle detection system using the same dataset.