Abstract: A vision frequency detection algorithm was proposed based on the eccentric rate vector of the edge information to deal with the feature characterization as well as classification and identification of the hybrid traffic. Utilizing the edge information extracted by the spatial-temporal context and the image highlights, an eccentric rate vector was built to characterize the features of the hybrid traffic. An extreme learning machine was used to establish a fast learning mechanism to achieve the rapid classification and identification, solving the problem that the support vector machine is difficult to achieve the real-time detection. The experiment results showed that the edge eccentric rate vector features of the hybrid traffic are distinct, the identification accuracy is above 93% while the calculation is rapid enough to meet the requirement of real-time detection.

Key words: engineering of communication and transportation system, hybrid traffic, feature characterization, edge eccentric rate vector, extreme learning machine