Recognition Based On Support Vector Machine Models

Automatic recognition and classification of vehicles is an important part of intelligent transportation system. It focuses on the recognition of vehicle at special place and in special time for traffic management, charging, regulation and statistics. Vehicle classification is a mature technology in developed countries, but due to a series of problems, this technology is unsuitable for application in our country. In order to realize the automatic charging and standardization of highway management, the research of vehicle recognition is imperative.In this paper, we focus on method of vehicle recognition based on algebra features of vehicle images. Firstly, the moving vehicles are picked out from background images using Background-Subtraction, and preprocessed into standardized images. Secondly, two kinds of vehicle features are extracted respectively by PCA and Weighted-LDA. Then the features are turned into a fusing feature by means of feature-fusion in complex space. Finally, by combining decision-tree support vector machine with nearest neighbor method and float-boosting algorithm, a new decision-tree support vector machine is built for vehicle recognition.The main work of this paper is:1. To get abundant information of feature, we put forward a feature fusion method, by which the two kinds of vehicle features extracted by PCA and Weighted-LDA are fused. The valid information of the two features is kept, and the redundant information is eliminated. The recognition rate is improved.2. Combining nearest neighbors method with decision-tree support vector machine, a new faster algorithm of classification is bring forward, which can not only reduce the train samples of support vector machine and speed up the train process, but also reduce the test samples and speed up the classification.3. We also combine the float-boosting algorithm with support vector machine and assemble some weak classifiers to build a boosted decision-tree support vector machine classifier. This classifier can decrease the error rate of vehicle recognition.With these methods, We construct a simple vehicle recognition system based on decision-tree support vector machine. The experiment results show that this system can work in higher speed and with higher accuracy.