Abstract
As a multi-classification problem, classification of moving vehicles has been studied by different statistical methods. These practical applications have various requirements, efficiencies, and performance, such as the size of training sample sets, convergence rate, and inseparable or ambiguous classification issues. With a reduction in its training time,the one-to-many support vector machine (SVM) method has an advantage over the standard SVM method by directly converting the binary classification problem into two multi-classification problems with short time and fast speed. When the number of training samples of a certain type is far less than the total number of samples, the accuracy of training, however, will be significantlydecreased,leading to theproblem of inseparable area. In this paper, the proposed nested one-to-one symmetric classification method on a fuzzy SVM symmetrically transforms the C multi-classification problems into the C(C-1)/2 binary classification problems with C(C-1)/2 classifiers, and solves the problem of inseparable area. According to the best combination factor of kernel function (γ, C) for the radial basis function (RBF) in the comparative experiments of training sample sets among the different algorithms, and the experimental results of many different training sample sets and test samples, the nested one-to-one symmetric classification algorithm on a fuzzy SVM for moving vehicle is able to obtain the best accuracy of recognition.
Highlights
Vehicle type classification is the key factor for diversion and guidance of modern intelligent traffic.It is necessary to overcome impacts on the quality and illumination of a video image because of the fast motion of vehicles, weather, and other factors
If the inseparable area contains only one type of sample, the area is assigned to one class; if it contains two types of samples, the binary classification fuzzy support vector machine would be used to divide the region and assign the corresponding category
(5) If the inseparable area contains only one type of sample, the area is assigned to one class; if it contains two types of samples, the binary classification fuzzy support vector machine
Summary
Vehicle type classification is the key factor for diversion and guidance of modern intelligent traffic. The size of training samples, the convergence rate of solving the classification problem, and inseparable regions or ambiguous classifications are the main influencing factors of multi-classifications methods. All of these facts motivate us to use the fuzzy support vector machine, to study the algorithms of the nested one-to-one symmetric classifier model, and to construct multi-classification classifiers for moving vehicles in the video domain. The organization of this paper is as follows: In Section 2, we will propose a nested one-to-one symmetric classification algorithm using a fuzzy support vector machine (FSVM), based on of the analysis of a one-to-many SVM and directed acyclic graph multi-classification algorithm. Construction of the Model of a Nested One-to-One Symmetric Classification Classifier on FSVM
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