A Framework for Wrong Way Driver Detection Using Optical Flow

In this paper, a solution to detect wrong-way drivers on highways is presented. The proposed solution is based on three main stages: Learning, Detection and Validation. Firstly, the orientation pattern of vehicles motion flow is learned and modeled by a mixture of gaussians. The second stage (Detection and Temporal Validation) applies the learned orientation model in order to detect objects moving in the lane’s opposite direction. The third and final stage uses an Appearance-based approach to ensure the detection of a vehicle before triggering an alarm. This methodology has proven to be quite robust in terms of different weather conditions, illumination and image quality. Some experiments carried out with several movies from traffic surveillance cameras on highways show the robustness of the proposed solution.