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In this paper we present a method to calibrate the extrinsic parameters of a monocular camera on a moving vehicle. The method is based on a homography between two camera shots. Therefore, only the road surface has to be visible in the pair of images. A reasonable definition of the vehicle coordinate system in combination with the use of epipolar geometry reduces the complexity to parameterize the underlying homography matrix. The extrinsic parameters are determined analytically by two correctly matched feature points located on the road surface. The final parameter set is determined by a recursive filter which considers various estimates over time. Results with a real-world video sequence indicate that the method is comparable to classical offline calibration techniques using objects of known geometry.

In this paper we present a method to compensate the image motion of a monocular camera on a moving vehicle in order to detect obstacles. Due to the camera motion, the road surface induces a characteristic image motion between two camera shots. The motion of the camera is determined by the use of odometric data received from the CAN-bus, and the position and orientation of the road is continuously estimated with camera self-calibration. This all leads to a motion field which is predicted based on homography. To prevent the drawbacks of the real camera perspective, different virtual camera perspectives are presented in combination with motion compensation. Possible virtual perspectives are the bird's eye view and image rectification. In addition, a non-linear camera model is used which does not limit the range of obstacle detection to a certain distance and efficiently uses the available image information.

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