Abstract
This paper presents a stratified auto-calibration framework for typical large surveillance set-ups including non-overlapping cameras. The framework avoids the need of any calibration target and purely relies on visual information coming from walking people. Since in non-overlapping scenarios there are no point correspondences across the cameras the standard techniques cannot be employed. We show how to obtain a fully calibrated camera network starting from single camera calibration and bringing the problem to a reduced form suitable for multi-view calibration. We extend the standard bundle adjustment by a smoothness constraint to avoid the ill-posed problem arising from missing point correspondences. The proposed framework optimizes the objective function in a stratified manner thus suppressing the problem of local minima. Experiments with synthetic and real data validate the approach.
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Picus, C., Micusik, B., Pflugfelder, R. (2010). From Single Cameras to the Camera Network: An Auto-Calibration Framework for Surveillance. In: Goesele, M., Roth, S., Kuijper, A., Schiele, B., Schindler, K. (eds) Pattern Recognition. DAGM 2010. Lecture Notes in Computer Science, vol 6376. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15986-2_3
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DOI: https://doi.org/10.1007/978-3-642-15986-2_3
Publisher Name: Springer, Berlin, Heidelberg
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