Vehicle Recognition Using Boosting Neural Network Classifiers
Limin Xia · 2006
The paper describes a method for vehicle recognition using a generic shape model and boosting neural network classifiers. The generic shape model, which is able to represent different vehicle classes, is derived by principal component analysis on a set of training shapes recovered automatically from 2D image sequences. The pose parameters and the shape parameters of the model are estimated by fitting the model to the vehicle in each image using Genetic algorithm, which are used to classify the vehicle. In order to improve the recognition accuracy and speed, we develop adaptive boosting neural network classifiers for vehicle recognition. Experiment results are presented for vehicle recognition, it is shown that our approach is more accuracy and faster than existing methods.