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A new image-based approach for fast and robust vehicle tracking from a moving platform is presented. Position, orientation, and full motion state, including velocity, acceleration, and yaw rate of a detected vehicle, are estimated from a tracked rigid 3-D point cloud. This point cloud represents a 3-D object model and is computed by analyzing image sequences in both space and time, i.e., by fusion of stereo vision and tracked image features. Starting from an automated initial vehicle hypothesis, tracking is performed by means of an extended Kalman filter. The filter combines the knowledge about the movement of the rigid point cloud's points in the world with the dynamic model of a vehicle. Radar information is used to improve the image-based object detection at far distances. The proposed system is applied to predict the driving path of other traffic participants and currently runs at 25 Hz (640 times 480 images) on our demonstrator vehicle.