Prevent collision with other vehicles is crucial for developing advanced driver assistance systems. Vision-based approaches for vehicle detection attract more attention than those using other sensors. In this study, we address the problem of detecting front vehicles in still images. Unlike traditional methods which mainly based on the holistic appearance of vehicles, we adopted a local part based model. We extended the Hidden Random Field (HRF) model to incorporate logistic regression classifiers into unary potentials. The proposed model was trained and tested on a set of real images captured by an on-board camera. The results showed that the effectiveness of the approach, and a better performance could be found when the vehicle was occluded by other vehicles.
['Xuetao Zhang', 'Nanning Zheng', 'Yongjian He', 'Fei Wang']
Vehicle detection using an extended Hidden Random Field model