The Research On Some Basic Algorithms And Technologies About Vehicle Recognition

Vehicle recognition is one of the basic problems in intelligent traffic system, because the background of vehicle image chops and changes and the vehicle outer shape is various, so, vehicle recognition is an unsolved problem. Its research attracts many authors, therefore, the vehicle recognition problem is studied. The main results and innovations can be described as follows:(1) First, the basic algorithms are studied: Neural network which can be used for vehicle recognition has parallel and quick prospect, in order to design stable and convergent neural network, the stable and convergence of neural networks need to be studied. So, the following two contents are received: stability and convergence research of bi-directional associative memory neural networks with perturbations and delays, The Stability of Perturbed Hopfield Neural Networks with Time Delays. Primary content analysis (PCA) has applicable value to vehicle recognition. For the parallel and quick feature, in order to extract the primary content of image, a neural network for extract the matrix eigenvalue and eigenvector is designed. Generally speaking, the computing weight for vehicle recognition is very heavy, the genetic algorithm can reduce the computing weight, therefore, a robust genetic algorithm with mutation parameters bounded by population's upgrowth is proposed.(2) A forward multi-layer neural network model that can be used for vehicle recognition is proposed. The model's network topology is presented, and this method is used in vehicle recognition successfully. The model integrates some results of neural network, fussy logic, pattern recognition etc, and recognize the object as a whole. The practice in the vehicle recognition using this model proofs that through supervised training, this model can correctly recognize the vehicle in spite of some interferential pixels.(3) A multiclass classifier that can be used for vehicle classification using support vector is designed. Different positive real numbers are used to denote different patterns. Through nonlinear mapping, the inputed samples are mapped into feature space, in this space, a linear mapping formula with unknown coefficient and bias is given, the nonzero output of the formula is correspondent to the pattern real number of the inputed sample. The plane corresponding to zero output of the formula is thought as reference plane. Under the condition of doing the best to minimize the difference between the linear mapping output real number and the true real pattern number of the input sample, the best are tried to maximize the distance (between the sample and the reference plane) between different patterns, the objective function of this goal is analogous to the objective function of support vector regression (SVR) formally, so, all the methods applied to SVR can be used for solving this problem, therefore, the coefficients and bias of the linear mapping formula are obtained. Through analyzing and testing the ways used to decide the pattern real number, we find that a better recognition right rate is received through deciding the pattern real number in the training process. Using the well-known IRIS data set to test the classifier,the practice indicates that the performance of this classifier is better than other methods based on LVQ neural network generally. Comparing with other methods such as 1-v-l, 1-v-r and decision tree method (DAGSVM) based on SVM, the running is quick, and the recognition right rate is high, approximately near to that of DAGSVM.(4) A new multiresolution vehicle recognition method is introduced. At first, it makes multi-wavelet division over the image, organizes the corresponding components of different resolution into a vector. Then, make a Fisher transform over the vectors. Recognition is based on the sum of the absolute distance between the vector of the unknown object and the vector of the template or based on the relative distance which is defined as the absolute distance between the vector of the unknown object and that of the template divided by the distanc...