Moving Vehicle Classification Using Cloud Model

Abstract

In this paper, we proposed a vehicle classification algorithm based on cloud model. Cloud model is a new theory which can express the relationship between randomness and fuzziness. Vehicle features, such as vehicle size, shape information, contour information and edge information are extracted for cloud model. Each vehicle class is expressed through cloud model parameters, such as Ex (expectation), En (entropy), with multi-dimensional feature. And cloud classification model is employed to judge the optimal class for each vehicle. Furthermore, attribute similarity is introduced to judge the weight of each feature in classification. Decision tree classifier is utilized for classification. The algorithm’s evaluations on video image series, the results show that cloud model ensures a promising and stable performance in recognizing these vehicle classes, and the algorithm can achieve accuracy and real-time.