Abstract
Applying advanced technologies to existing problem domains is a highly desirable approach in many research areas. Among these techniques, image processing has been shown useful in transportation fields for such tasks as traffic pattern recognition, data collection, accident detection, and pavement evaluation. The integrated model with artificial neural networks (ANNs) has promising potential applications. The image processing and ANN model are combined to explore the feasibility of vehicle classification in real-world situations. Three methods were developed during the research process: ground segmentation, background subtraction, and window segmentation. The first two methods were used to separate the objects of scene and nonscene from the actual traffic image. To reduce the complexity of neural networks, the image was divided into 16 windows and three characteristics (occupation rates of vehicles, of horizontal image lines, and of vertical image lines) of each window were extracted to generate 48 factors as the input units of the neural network. The backpropagation ANN model with one hidden layer is employed. The experiments show that the accurate recognition rates of heavy vehicles, small cars, and motorcycles are 98.5, 96.92, and 91.94 percent, respectively. The result implies the remarkable applicability of the proposed methods in transportation areas.
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