Accurate Camera Calibration for Off-line, Video-Based Augmented Reality
Simon Gibson, Jon Cook, Toby Howard, Roger Hubbold, and Dan Oram.
IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR 2002), Darmstadt, Germany, September 2002 (to appear).
Abstract
Camera tracking is a fundamental requirement for video-based Augmented Reality applications. The ability to accurately calculate the intrinsic and extrinsic camera parameters for each frame of a video sequence is essential if synthetic objects are to be integrated into the image data in a believable way. In this paper, we present an accurate and reliable approach to camera calibration for off-line video-based Augmented Reality applications. We first describe an improved feature tracking algorithm, based on the widely used Kanade-Lucas-Tomasi tracker. Estimates of inter-frame camera motion are used to guide tracking, greatly reducing the number of incorrectly tracked features. We then present a robust hierarchical scheme that merges sub-sequences together to form a complete projective reconstruction. Finally, we describe how RANSAC-based random sampling can be applied to the problem of self-calibration, allowing for more reliable upgrades to metric geometry. Results of applying our calibration algorithms are given for both synthetic and real data
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