Research On Gabor Filters With Applications To Vehicle Detection And Vehicle Classification

Robust and reliable vehicle detection and vehicle classification are important issues with applications to Intelligent Transportation Systems (ITS). And we introduce Gabor filters here, since they have been successfully applied for pattern recognition. In this paper we focus our attention on the works in this field, which include four parts: an improved Gabor feature extraction algorithm based on feature weighting, infrared vehicle detection with Gabor filters and Support Vector Machines (SVM), vehicle classification based on Gabor filters and edge features and a practical design for parameters of Gabor filters based on a certain orientation.First, an improved Gabor feature extraction algorithm based on feature weighting is proposed. It weights the raw features derived from 2D Gabor filters according to their own degree of dispersion, which can enhance the effect of the features whose degree of dispersion is relatively small but also can widely used the statistical information of sample images. The experiment results indicate that the proposed method is superior to conventional ones in terms of robustness and discrimination ability. Thus it is fit for the recognition of images with poor quality.Second, an infrared vehicle detection method is developed which contains two main steps: driven hypothesis generation and hypothesis verification. In the hypothesis generation step, possible image locations where vehicles might be present are hypothesized by pixel-dependent threshold selection and edge detection. Hypothesis verification verifies those hypothesis using Gabor filters for feature extraction and SVM for classification. The feature weighting technique mentioned above is used here. This method was tested under four different videos does show visible improvements both in diminishing error rate and robustness.Third, we put forward a novel non-even sampling of Gabor features for classification on the basis of the edge features in vehicles to avoid the heavy computation and memory requirements caused by Gabor feature vectors. In stead of extracting Gabor features at sub-sampled positions of rectangular grid points in general applications, we adopt different sampling intervals on key points and assistant points according to the geometrical features in vehicles. The experimental data show that the method proposed here is simple and effective for both dimension reduction and image representation.Finally, a practical design for parameters of Gabor filters based on a certain orientation is presented. Experiment-based and optimization-based methods are two popular ways in this domain. However, the parameters are not precise in the former and the algorithm is too complex in the latter. To avoid these defects, a more practical one is given. Our main idea is to set the orientation parameters manually based on directional characters in Gabor features, then at each orientation search the optimal single Gabor filter. The parameters of Gabor filters we get are close to optimization, and the algorithm is simple and data dependent as well. The experimental data show that this method is available and efficient.