OCPAD - Occluded Checkerboard Pattern Detector

Fürsattel P, Deitsch S, Placht S, Balda M, Maier A, Riess C (2016)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2016

Publisher: IEEE

Pages Range: 1--9

Event location: Lake Placid, NY US

ISBN: 978-1-5090-0641-0

DOI: 10.1109/WACV.2016.7477565


Many camera calibration techniques require the detection of a pattern with known geometry, e.g., a checkerboard. Typically, the pattern must be fully contained in the field of view. This brings several limitations, one of which is that lens distortion can not reliably be estimated in outer image regions. This paper presents the occluded checkerboard pattern detector (OCPAD) to find checkerboards, even in a) low-resolution images, b) images with high lens distortion and if c) the pattern is partly occluded or not completely within the field of view. We exploit that checkerboards can easily be represented by a graph. We use graph matching to find the largest partial checkerboard in the image. Our detector complements a state-of-the-art calibration algorithm. Quantitatively, detection rates are considerably improved over the state-of-the-art. Additionally, estimation of lens distortion is greatly improved at outer image regions. Here, the reprojection error is improved by up to 50%.

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How to cite


Fürsattel, P., Deitsch, S., Placht, S., Balda, M., Maier, A., & Riess, C. (2016). OCPAD - Occluded Checkerboard Pattern Detector. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision (pp. 1--9). Lake Placid, NY, US: IEEE.


Fürsattel, Peter, et al. "OCPAD - Occluded Checkerboard Pattern Detector." Proceedings of the IEEE Winter Conference on Applications of Computer Vision, Lake Placid, NY IEEE, 2016. 1--9.

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