Improving the performance of on-road vehicle detection by combining Gabor and wavelet features

Abstract

Appearance-based methods represent a promising research direction to the problem of vehicle detection. These methods learn the characteristics of the vehicle class from a set of training images which capture the variability in vehicle appearance. First, training images are represented by a set of features. Then, the decision boundary between the vehicle and nonvehicle classes is computed by modelling the probability distribution of the features In each class or through learning. The purpose of this study is to investigate the effectiveness of two important types of features for vehicle detection based on Haar wavelets and Gabor filters. In both cases, the decision boundary is computed using support vector machines (SVMs). Wavelet features encode edge information. Gabor filters provide a mechanism for obtaining orientation and scale tunable edge and line detectors. Our experimental results and comparisons using real data illustrate the effectiveness of both types of features for vehicle detection, with Gabor features performing the better. The two feature sets yield different misclassification errors which led us to the idea of combining them for improving performance. The combined set of features outperformed each feature set alone on completely novel test images.