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Autonomous extraction of information from traffic video surveillance has received widespread attention in recent years due to the capability of analyzing massive data persistently. In this paper, we present a system for extracting vehicle information from traffic video sequence build a framework for defining driver behavior models. We detect the sedans and split them into two categories - taxi and otherwise, according to the color and size. The labeled cars from two categories form two exemplars of driver behavior - professional and standard. We use these two exemplars to compute the unknown parameters of the Hidden Markov Model (HMM) and demonstrate the quantitative discrimination of these two categories, which can be used for many applications of intelligent transportation system, such as abnormality detection and transportation planning. We also demonstrate applications which utilize the proposed model …