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Fast Detection of Moving Objects in Complex Scenarios

Clemens Rabe, Uwe Franke, Stefan Gehrig


Abstract

More than one third of all traffic accidents with injuries occur in urban areas, especially at intersections. A suitable driver assistance system for such complex situations requires the understanding of the scene, in particular a reliable detection of other moving traffic participants. This contribution shows how a robust and fast detection of relevant moving objects is obtained by a smart combination of stereo vision and motion analysis. This approach, called 6D Vision, estimates location and motion of pixels simultaneously which enables the detection of moving objects on a pixel level. Using a Kalman filter attached to each tracked pixel, the algorithm propagates the current interpretation to the next image. In addition, a Kalman filter based ego-motion compensation is described that takes advantage of the 6D information. This precise information enables us to discriminate between static and moving objects exactly and to obtain a better prediction. This speeds up tracking and a real-time implementation is achieved. Examples of critical situations in urban areas exhibit the potential of the 6D Vision concept which can also be extended to robotics applications.

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