Front Vehicle Detection is the key and difficult point of the key technology research for the
intelligent vehicle. In this paper the digital image is firstly binarized through the image enhancement,
threshold segmentation and noise eliminating along with recent patents described. Then hypothesis
generation is done according to the structure, shape, aspect ratio of the vehicle and shadow at the bottom of the vehicle.
On this basis, features extraction is performed with a Gabor features extraction method based on the improved features
weightings for the selected vehicle samples and background samples. The extracted features vector is regarded as the input
of the support vector machine (SVM) for training. Finally, the trained SVM classifier is used to conduct the vehicle
classification and recognition. Thus the vehicle detection is completed. The experimental results show that the approach
can improve the recognition rate and the robustness of preceding vehicle detection for the intelligent vehicle.
Keywords: Computer vision, intelligent vehicle, SVM, vehicle detection.
Rights & PermissionsPrintExport