Log Gabor Filter Based Feature Detection in Image Verification Application

In recent years due to low-cost, flexibility, and potential toward collision avoidance the Vehicle and Non Vehicle identification based on image processing bring more attention. In particular, vehicle verification is especially challenging on account of the heterogeneity of vehicles in color, size, pose, etc. Image based vehicle verification is usually addressed as a supervised classification problem. Specifically, descriptors using Gabor filters have been reported to show good performance in this task. However, Gabor functions have a number of drawbacks relating to their frequency response. The main contribution of this paper is the proposal and evaluation of a new descriptor based on the alternative family of log-Gabor functions for vehicle verification, as opposed to existing Gabor filter-based descriptors. These filters are theoretically superior to Gabor filters as they can better represent the frequency properties of natural images. In this paper the classifications is done using SVM and Random Forest. in this Random Forest will classify vehicle and non vehicle and Human on road. Whereas SVM deals with only vehicle and non vehicle.