Optimal omnidirectional sensor for urban traffic diagnosis in crossroads

In this paper we present an optimal omnidirectional visual sensor which can replace perspective camera network for traffic diagnosis. The proposed system has the advantage, by the number and the designed mirror, to generate a single view of the crown and junction ways of the crossroads by maximizing the number of useless pixels. So, the percentage of pixels utilized directly for subsequent phases of image processing is optimal. We describe the methodology used to design such a sensor. In addition, to assess our sensor, we also developed image processing methods that provide useful indicators for estimating the state of the traffic as the crossroads occupancy rate, the vehicle speed and the flow of vehicles. Finally, we compare this optimal sensor to others that consist of parabolic, hyperbolic or spherical mirror to observe the scene. We prove that optimal sensor has better results than others.