Efficient Feature Extraction for Highway Traffic Density Classification

Traffic density estimation is one of the most challenging problems in Intelligent Transportation Systems. In this paper, we estimate the traffic flow density based on classification. Various new efficient features are introduced for distinguishing between different traffic states, includingnumb er of key-points, edges of difference-image and moving edges. These features describe the traffic flow without any need to individual vehicles detection and tracking. We experiment our proposed approach on a standard database and some realvideos from Tehran roads. The results show high accuracyperformance of our method, even in changes of environmentalconditions (e.g., lighting), by using efficient features. Duo to low computational cost, our proposed approach for traffic density estimation is applicable in real time applications

کلیدواژه ها:

Feature extraction ، Intelligent Transportation System ، Support Vector Machine ، traffic density classification