Wrongway Drivers Detection Based on Optical Flow

In this paper it is described a solution to detect wrong way drivers on highways. The proposed solution is based on three main stages: learning, detection and validation. In the first stage, the orientation pattern of vehicles motion flow is learned and modelled by a mixture of Gaussians. The second stage (detection and temporal validation) makes use of the learned orientation model to detect objects moving on 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.