Vehicle Detection Using Gabor Filter

On road vehicle detection is the main problem for some applications such as driver assistance systems, autonomous and self-guided vehicles. Vehicle or non vehicle classification is the important issue in the case of vehicle detection, Gabor filters are having good performance in this case. Feature extraction and edge detection are performed to do this vehicle or non vehicle classification. Support vector machine classifiers are used to do this feature extraction. Vehicle detection algorithms are mainly having two stages, one is hypothesis generation and another one is hypothesis verification. Hypothesis generation is nothing but the quick search on the potential location of the vehicle in the image. Hypothesis verification stage is used to verify the correctness of the vehicle candidates provided by the previous stage. And also in this paper we propose accuracy of Gabor filter as a function several parameters such as number of scales, minimum wavelength, adaptable wavelength and also the number of orientations.