Integrated Real-Time Vision-Based Preceding Vehicle Detection in Urban Roads

This paper presents a real-time algorithm for a vision-based preceding vehicle detection system. The algorithm contains two main components: vehicle detection with various vehicle features, and vehicle detection verification with dynamic tracking. Vehicle detection is achieved using vehicle shadow features to define a region of interest (ROI). After utilizing methods such as histogram equalization, ROI entropy and mean of edge image, the exact vehicle rear box is determined. In the vehicle tracking process, the predicted box is verified and updated. Test results demonstrate that the new system possesses good detection accuracy and can be implemented in real-time operation.