To improve traffic safety it is important to evaluate the safety of roads and intersections. Today this requires a large amount of manual labor so an automated system using cameras would be very beneficial. We focus on the geometric part of the problem, that is, how to get accurate three-dimensional data from images of a road or an intersection. This is essential in order to correctly identify different events and incidents, for example to estimate when two cars gets dangerously close to each other. The proposed method uses a standard tracker to find corresponding points between frames. Then a RANSAC-type algorithm detects points that are likely to belong to the same vehicle. To fully exploit the fact that vehicles rotate and translate only in the ground plane, the structure from motion is estimated using an optimization approach based on the L∞-norm. The same approach also allows for easy setup of the system by estimating the camera orientation relative to the ground plane. Promising results for real-world data are presented.
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