Abstract
In this paper, we proposed an effective overtaking vehicle detection system. Using image from car-mounted camera as input, detect vehicle’s rear-view and side-view on actual driving scene to achieve overtaking vehicle detection. First, compute MB-LBP feature of image, then use cascade classifier as a detector trained by AdaBoost algorithm. Differing from the traditional detection approach, a detector using 3 different aspect ratios and finding the varying scales is established so as to overcome the fact that in real driving situations, the aspect ratio of the tire appearing in the image keeps varying. Using cascade classifier helps enhance the detection speed and keep the discriminative features so as to enhance the detection rate. Finally, both the information of the rear-view and the wheel of a vehicle are used to mark the position of it in the image. The performance of wheel detector is demonstrated with the precision rate as 0.91 and recall rate as 0.96. After wheel detection, the wheel matching is proposed which can be further applied for the overtaking vehicle localization. We achieve good detection results with two detectors.