cvpr cvpr2013 cvpr2013-84 cvpr2013-84-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Nathan Jacobs, Mohammad T. Islam, Scott Workman
Abstract: We propose cloud motion as a natural scene cue that enables geometric calibration of static outdoor cameras. This work introduces several new methods that use observations of an outdoor scene over days and weeks to estimate radial distortion, focal length and geo-orientation. Cloud-based cues provide strong constraints and are an important alternative to methods that require specific forms of static scene geometry or clear sky conditions. Our method makes simple assumptions about cloud motion and builds upon previous work on motion-based and line-based calibration. We show results on real scenes that highlight the effectiveness of our proposed methods.
[1] A. Abrams, N. Fridrich, N. Jacobs, and R. Pless. Participatory integration of live webcams into gis. In COM.GEO, 2010.
[2] A. Abrams, J. Tucek, J. Little, N. Jacobs, and R. Pless. Lost: Longterm observation of scenes (with tracks). In IEEE Workshop on Applications of Computer Vision, 2012.
[3] P. Beardsley and D. Murray. Camera calibration using vanishing points. In British Machine Vision Conference, 1992.
[4] J. Bergen, P. Anandan, K. Hanna, and R. Hingorani. Hierarchical model-based motion estimation. In European Conference on Computer Vision, 1992.
[5] M. D. Breitenstein, H. Grabner, and L. V. Gool. Hunting nessie—real-time abnormality detection from webcams. In IEEE Workshop on Visual Surveillance, 2009.
[6] X. Cao and H. Foroosh. Camera calibration and light source orientation from solar shadows. Computer Vision and Image Understanding, 105(1), 2007.
[7] B. Caprile and V. Torre. Using vanishing points for camera calibration. International Journal ofComputer Vision, 1990.
[8] F. Cozman and E. Krotkov. Robot localization using a computer vision sextant. In IEEE International Conference on Robotics Applications, volume 1, 1995.
[9] B. K. Horn and E. Weldon Jr. Direct methods for recovering motion. International Journal of Computer Vision, 1988.
[10] N. Jacobs, B. Bies, and R. Pless. Using cloud shadows to infer scene structure and camera calibration. In IEEE Conference on Computer Vision and Pattern Recognition, 2010.
[11] N. Jacobs, W. Burgin, N. Fridrich, A. Abrams, K. Miskell, B. H. Braswell, A. D. Richardson, and R. Pless. The global network of outdoor webcams: Properties and applications. In ACM SIGSPATIAL, 2009.
[12] N. Jacobs, N. Roman, and R. Pless. Toward fully automatic geo-location and geo-orientation of static outdoor cameras. In IEEE Workshop on Applications of Computer Vision, 2008.
[13] N. Jacobs, S. Satkin, N. Roman, R. Speyer, and R. Pless. Geolocating static cameras. In IEEE International Conference on Computer Vision, 2007.
[14] S. J. Kim, J.-M. Frahm, and M. Pollefeys. Radiometric calibration with illumination change for outdoor scene analysis. In IEEE Conference on Computer Vision and Pattern Recognition, 2008.
[15] J.-F. Lalonde, S. G. Narasimhan, and A. A. Efros. What does the sky tell us about the camera? In European Conference on Computer Vision, 2008.
[16] J. Luo and S. Etz. A physical model-based approach to detecting sky in photographic images. IEEE Transactions on Image Processing, 11(3), 2002.
[17] T. McLoughlin, R. Laramee, R. Peikert, F. Post, and M. Chen. Over two decades of integration-based, geometric flow visualization. Computer Graphics Forum, 29(6), 2010.
[18] B. Prescott and G. McLean. Line-based correction of radial lens distortion. Graphical Models and Image Processing, 59(1), 1997.
[19] Y. Sheikh, N. Haering, and M. Shah. Shape from dynamic texture for planes. In IEEE Conference on Computer Vision and Pattern Recognition, 2006.
[20] K. Sunkavalli, F. Romeiro, W. Matusik, T. Zickler, and H. Pfister. What do color changes reveal about an outdoor scene? In IEEE Conference on Computer Vision and Pattern Recognition, 2008.
[21] T. Tian, C. Tomasi, and D. Heeger. Comparison of approaches to egomotion computation. In IEEE Conference on Computer Vision and Pattern Recognition, 1996.
[22] M. Wilczkowiak, E. Boyer, and P. Sturm. Camera calibration and 3d reconstruction from single images using parallelepipeds. In IEEE International Conference on Computer Vision, 2001 .
[23] J. Wright and R. Pless. Analysis of persistent motion patterns using the 3d structure tensor. In IEEE Workshop on Applications of Computer Vision, 2005. 1 1 13 3 34 45 80 8 LOST26342381536239132 1394150316371453 042 2540314267457438017504 LOST2 61504293 29314297 4 523 71 743 10 6271809 4329 4523 71840 231 2 MARK 1 4 6 1715432 9207641832 23285241426181 751 43261373 51832 6532971 35702 64308961 52396 8495304256014276 4218560173 5 LOST 2 101814158 16 9132 LOST 21 #