Preceding vehicle recognition based on learning from sample images

Preceding vehicle recognition is an important enabling technology for developing a driver assistance system and an autonomous vehicle system. However, this is difficult for computer vision to achieve because of the variety of shapes and colors in which vehicles are made. In this paper, we propose a novel vision-based preceding vehicle recognition method, which has the capability of recognizing a wide selection of vehicles. In the proposed method, classifiers learned from "vehicle" training samples and "nonvehicle" training samples are used to enable recognition. We also propose a novel classification method, the "multiclustered modified quadratic discriminant function" (MC-MQDF). The MC-MQDF is capable of estimating the complex distribution due to the variety of different possible appearances for preceding vehicles. In order to confirm the feasibility of recognizing various vehicles, and to demonstrate the advantage of the MC-MQDF over the MQDF, classification experiments were carried out using the images of various vehicles. In a complex distribution test including a variety of vehicles, the classification rate for the MC-MQDF was approximately 98%, whereas the classification rate for the ordinary MQDF technique was approximately 93%. This supports the superiority of the MC-MQDF technique over the MQDF technique, and demonstrates the feasibility of recognizing a variety of different vehicles.