Vision-based Frontal Vehicle Detection and Tracking

This paper presents a vision-based driver assistance system composing of vehicle detection using knowledge-based method and vehicle tracking using Kalman filtering. First, a preceding vehicle is localized by a proposed detection scheme, consisting of shadow detection and brake lights detection. Second, the possible vehicle region is extracted for verification. Symmetry analysis includes contour and brake lights symmetries are performed and followed by an asymmetry contour analysis in order to obtain vehicle's center. The center of vehicle is tracked continuously using Kalman filtering within a predicted sub-window in consecutive frames. It reduces the scanning process and maximizes the computational speed of vehicle detection. Simulation results demonstrate good performance of the proposed system.