A Multi-Feature Fusion Method for Forward Vehicle Detection with Single Camera

Vehicle detection is very important for Advanced Driver Assistance System. This paper focused on improving the performance of vehicle detection system with single camera and proposed a multi-feature fusion method for forward vehicle detection. The shadow and edges of the vehicle are the most important features, so they can be utilized to detect vehicle at daytime. The shadow and edge features were segmented accurately by using histogram analysis method and adaptive dual-threshold method respectively. The initial candidates were generated by combining edge and shadow features, and these initial candidates were further verified using an integrated feature based on the fusion of symmetry, texture and shape matching degree features. The weight of each feature was determined by the Fisher criterion, and the non-vehicle initial candidates were rejected by a threshold. The experimental results show that the proposed method could be adapt to different illumination circumstances robustly and improve the accuracy of forward vehicle detection.