This paper concerns recognizing 3-dimensional object using proposed multi-layer block model. In particular, we aim to achieve desirable recognition performance while restricting the computational load to a low level using 3-step feature extraction procedure. An input image is first precisely partitioned into hierarchical layers of blocks in the form of base blocks and overlapping blocks. The hierarchical blocks are merged into a matrix, with which abundant local feature information can be obtained. The local features extracted are then employed by the kernel based support vector machines in tournament for enhanced system recognition performance while keeping it to low dimensional feature space. The simulation results show that the proposed feature extraction method reduces the computational load by over 80% and preserves the stable recognition rate from varying illumination and noise conditions.

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 images. However, this method does have a problem. Because it is view-based, our system requires model images of the target vehicles. Collecting real images of the target vehicles is generally a time consuming and difficult task. To ease the task of collecting images of all target vehicles, we apply our system to computer graphics (CG) models to recognize vehicles in real images. Through outdoor experiments, we have confirmed that using CG models is effective than collecting real images of vehicles for our system. Experimental results show that CG models can recognize vehicles in real images, and confirm that our system can classify vehicles.

To enhance safety and traffic efficiency, a driver assistance system and an autonomous vehicle system are being developed. A preceding vehicle recognition method is important to develop such systems. In this paper, a vision-based preceding vehicle recognition method, based on supervised learning from sample images is proposed. The improvement for Modified Quadratic Discriminant Function (MQDF) classifier that is used in the proposed method is also shown. And in the case of road environment recognition including the preceding vehicle recognition, many researches have been reported. However in those researches, a quantitative evaluation with large number of images has rarely been done. Whereas, in this paper, over 1,000 sample images for passenger vehicles, which are recorded on a highway during daytime, are used for an evaluation. The evaluation result shows that the performance in a low order case is improved from the ordinary MQDF. Accordingly, the calculation time is reduced more than 20% by using the proposed method. And the feasibility of the proposed method is also proved, due to the result that the proposed method indicates over 98% as classification rate.