Spherical Panorama Image Generation Method using Homography and Tracking Algorithm

호모그래피와 추적 알고리즘을 이용한 구면 파노라마 영상 생성 방법

  • 아나르 (숭실대학교 미디어학과) ;
  • 최형일 (숭실대학교 미디어학과)
  • Received : 2016.12.15
  • Accepted : 2017.02.26
  • Published : 2017.03.28


Panorama image is a single image obtained by combining images taken at several viewpoints through matching of corresponding points. Existing panoramic image generation methods that find the corresponding points are extracting local invariant feature points in each image to create descriptors and using descriptor matching algorithm. In the case of video sequence, frames may be a lot, so therefore it may costs significant amount of time to generate a panoramic image by the existing method and it may has done unnecessary calculations. In this paper, we propose a method to quickly create a single panoramic image from a video sequence. By assuming that there is no significant changes between frames of the video such as in locally, we use the FAST algorithm that has good repeatability and high-speed calculation to extract feature points and the Lucas-Kanade algorithm as each feature point to track for find the corresponding points in surrounding neighborhood instead of existing descriptor matching algorithms. When homographies are calculated for all images, homography is changed around the center image of video sequence to warp images and obtain a planar panoramic image. Finally, the spherical panoramic image is obtained by performing inverse transformation of the spherical coordinate system. The proposed method was confirmed through the experiments generating panorama image efficiently and more faster than the existing methods.


Panorama;Spherical Coordinate System;Lucas-Kanade;Feature Point Tracking;Homography


Supported by : 중소기업청


  1. R. Szeliski, Image alignment and stitching: A tutorial, Technical Report MSR-TR-2004-92, Microsoft Research, December 2004.
  2. A. Bartoli, N. Dalal, B. Bose, and R. Horaud. "From video sequences to motion panoramas," In IEEE Workshop on Motion and Video Computing, December 2002.
  3. Y. Li, L-Q. Xu, G. Morrison, C. Nightingale, and J. Morphett. "Robust panorama from mpeg video," In IEEE International Conference on Multimedia and Expo 2003 (ICME '03), pp.81-84, 2003.
  4. D. Steedly, C. Pal, and R. Szeliski, "Efficiently Registering Video into Panoramic Mosaics," Tenth IEEE International Conference on Computer Vision (ICCV'05) IEEE, Vol.1, pp.1300-1307, 2005.
  5. M. Brown and D. Lowe, "Automatic panoramic image stitching using invariant features," International Journal of Computer Vision, Vol.74, pp.59-73, 2007.
  6. P. H. S. Torr and A. Zisserman, "Feature Based Methods for Structure and Motion Estimation," Vision Algorithms: Theory and Practice, Springer, Vol.1883, pp.278-294, 2000.
  7. M. Irani and P. Anandan, "About direct methods," In B. Triggs, A. Zisserman, and R. Szeliski, editors, Vision Algorithms: Theory and Practice, number 1883 in LNCS, Springer-Verlag, Corfu, Greece, pp.267-277, September 1999.
  8. D. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, Vol.60, No.2, pp.91-110, 2004.
  9. H. Bay, T. Tuytelaars, and L. V. Gool, "Surf: Speeded up robust features," European Conference on Computer Vision, Vol.3951, pp.404-417, 2006.
  10. E. Rosten and T. Drummond, "Machine learning for high-speed corner detection," In Proc. 9th European Conference on Computer Vision (ECCV'06), Graz, May 2006.
  11. B. D. Lucas and T. Kanade, "An Iterative Image Registration Technique with an Application to Stereo Vision," International Joint Conference on Artificial Intelligence, pp.674-679, 1981.
  12. J. Y. Bouguet, Pyramidal Implementation of the Lucas Kanade Feature Tracker Description of the algorithm, Technical Report, Intel Microprocessor Research Labs, 1999.
  13. M. Fischler and R. Bolles, "Random sample consensus: A paradigm for model fitting with application to image analysis and automated cartography," Communications of the ACM, Vol.24, pp.381-395, 1981.
  14. M. Marius and D. Lowe, "Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration," International Conference on Computer Vision Theory and Applications, Vol.1, pp.331-340, 2009.
  15. O. Enqvist, F. Jiang, and F. Kahl, "A brute-force algorithm for reconstructing a scene from two projections," Computer Vision and Pattern Recognition(CVPR), 2011.