A front vehicle detection algorithm for intelligent vehicle based on improved Gabor filter and SVM

作者：Zhang, Zhonghua; Yu, Xuecai^*; You, Feng; Siedel, George; He, Wenqiang; Yang, Lifang

来源：Recent Patents on Computer Science, 2015, 8(1): 32-40.

DOI：10.2174/2213275907666141023220519

• Features extraction
• Hypothesis generation
• Key technologies
• Noise eliminating
• Preceding vehicle detections
• Threshold segmentation
• Vehicle classification
• Vehicle detection

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.

• 出版日期2015
• 单位University of Michigan,Ann Arbor; University of Michigan Ann Arbor; University of Michigan, Ann Arbor; 华南理工大学; University of Michigan-Ann Arbor