IEICE - Transactions on Information and Systems
A view-based statistical system for multi-view face detection and pose estimation
Image and Vision Computing
Dynamic Tracking System through PSO and Parzen Particle Filter
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
A hybrid method for robust car plate character recognition
Engineering Applications of Artificial Intelligence
A Chinese license plate recognition system
ASMCSS'09 Proceedings of the 3rd International Conference on Applied Mathematics, Simulation, Modelling, Circuits, Systems and Signals
Online boosting for vehicle detection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Vehicle logo recognition using a SIFT-based enhanced matching scheme
IEEE Transactions on Intelligent Transportation Systems
Vehicle model recognition from frontal view image measurements
Computer Standards & Interfaces
Automatic vehicle detection using statistical approach
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Multi-resolution image analysis for vehicle detection
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
Classification of vehicle type and make by combined features and random subspace ensemble
International Journal of Computational Vision and Robotics
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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.