Vehicle Class Recognition Using 3d Cg Models

This paper describes a robust method for recognizing vehicle classes. In our previous work, we have developed a vehicle recognition system based on local-feature configuration, which is a generalization of the eigen-window method. This system could recognize one vehicle class very accurately, but there have been limitations in recognizing several classes, when they are quite similar to each other. In this paper, we describe the improvements of our recognition system to distinguish four classes, namely sedan, wagon, mini-van and hatchback. The system requires training images of all target vehicle classes. These training images are easily created using a 3-dimentional computer graphic (3D-CG) tool. Using CG training images dispenses with much of the trouble of collecting real training images, and causes no effect on accuracy. Outdoor experimental results have shown that this recognition system can classify vehicles in real images with an accuracy of more than 80%

Vehicle Class Recognition of Street-Parking Vehicles from Side-View Range Images

This paper describes a novel method for recognizing the classes of street-parking vehicles. We have already developed the following two systems: one is vehicle recognition system based on local-feature configuration, and the other is detecting street-paring vehicles from side-view range images. In this paper, we combine these two systems to develop a new system with which we can not only count the number of street-parking vehicles but also recognize their class of vehicle type such as sedan, wagon, mini-van or so. We have confirmed that our classification algorithm is still robust on range images, performing outdoor experiments. Our system can recognize four vehicle classes of sedan, wagon, mini-van and hatchback from outdoor range images with accuracy of about 80%

PAPER Special Section on Information System Technologies for ITS

This paper describes our vehicle classification system, which is based on local-feature configuration. We have already demonstrated that our system works very well for vehicle recognition in outdoor environments. The algorithm is based on our previous work, which is a generalization of the eigen-window method. This method has the following three advantages: (1) It can detect even if parts of the vehicles are occluded. (2) It can detect even if vehicles are translated due to veering out of the lanes. (3) It does not require segmentation of vehicle areas from input image