Vision-based vehicle detection for a driver assistance system

On the basis of a cost-effective embedded system, this work implements a preceding vehicle detection system by using computer vision technologies. The road scenes are acquired with a monocular camera. The features of the vehicle in front are extracted and recognized by the proposed refined image processing algorithm, and a tracking process based on optical flow is also applied for reducing the complexity of computing. The system also provides the longitudinal distance information for the further function of adaptive cruise control. Moreover, voice alerts and image recording will be activated if the distance is less than the safe range. A statistical base of 100 video road images are tested in our experiments; the natures of the vehicles include sedan, minivan, truck, and bus. The experimental results show that the proportion of correct identifications of proceeding vehicles is above 95.8%, testing on highways in the daytime. Experimental results also indicate that the system correctly identifies vehicles in real time.