Forward vehicle detection based on shadow features and adaboost

A single camera based on-road forward vehicle detection system was developed to detect vehicles in front of the driver in a driver assistance system to improve vehicle safety.The shadows underneath vehicles are detected to generate vehicle hypotheses.An adaptive road threshold method and a dark areas merge approach are used to resolve the grayscale variation on the road and the distortion of the shadow border.A gradient-based adaboost method is used to verify the hypotheses.Kalman filters are then used to track objects to improve system performance.The system was tested with series of images taken on roads.The results show that the system can effectively detect vehicles in front of the driver on real time.