Recognizing Multiple Billboard Advertisements in Videos

  • Naoyuki Ichimura
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4319)


The sponsors for events such as motor sports can install billboard advertisements at event sites in return for investments. Checking how ads appear in a broadcast is important to confirm the effectiveness of investments and recognizing ads in videos is required to make the check automatic. This paper presents a method for recognizing multiple ads. After obtaining point correspondences between a model image and a scene image using local invariants features, we separate the point correspondences of an instance of an ad by calculating a homography using RANSAC. To make the use of RANSAC feasible, we develop two techniques. First, we use the ratio of distances of descriptors to reject outliers and introduce a novel scheme to set a threshold for the ratio of distances. Second, we incorporate an evaluation on appearances of ads into RANSAC to reject the homographies corresponding to appearances of ads which are never observed in actual scenes. The detail of a recognition algorithm based on these techniques is shown. We conclude with experiments that demonstrate recognition of multiple ads in videos.


Model Image Illumination Change Scene Image Point Correspondence Normalize Cross Correlation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Harris, C., Giraudon, G.: A combined corner and edge detector. In: Proc. 4th Alvey Vis. Conf., pp. 147–151 (1988)Google Scholar
  2. 2.
    Schmid, C., Mohr, R.: Local greyvalue invariants for image retrieval. IEEE Trans. PAMI 19(5), 530–535 (1997)Google Scholar
  3. 3.
    Lowe, D.: Object recognition from local scale-invariant features. In: Proc. ICCV, vol. 2, pp. 1150–1157 (1999)Google Scholar
  4. 4.
    Brown, M., Lowe, D.: Invariant features from interest point groups. In: Proc. BMVC, pp. 253–262 (2002)Google Scholar
  5. 5.
    Sivic, J., Zisserman, A.: Video google: a text retrieval approarch to object matching in videos. In: Proc. ICCV, vol. 2, pp. 1470–1477 (2003)Google Scholar
  6. 6.
    Brown, M., Lowe, D.: Recognising panoramas. In: Proc. ICCV, vol. 2, pp. 1218–1225 (2003)Google Scholar
  7. 7.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)CrossRefGoogle Scholar
  8. 8.
    Mikolajczyk, K., Schmid, C.: Scale & affine invariant interest point detectors. IJCV 60(1), 63–86 (2004)CrossRefGoogle Scholar
  9. 9.
    Brown, M., Szeliski, R., Winder, S.: Multi-image matching using multi-scale oriented patches. In: Proc. CVPR, vol. 1, pp. 510–517 (2005)Google Scholar
  10. 10.
    Quelhas, P., Monay, F., Odobez, J.M., Gatica-Perez, D., Tuytelaars, T., Van Gool, L.: Modeling scenes with local descriptors and latent aspects. In: Proc. ICCV, vol. 1, pp. 883–890 (2005)Google Scholar
  11. 11.
    Schaffalitzky, F., Zisserman, A.: Viewpoint invariant texture matching and wide baseline stereo. In: Proc. ICCV, vol. 1, pp. 636–643 (2001)Google Scholar
  12. 12.
    Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: Proc. BMVC, pp. 384–393 (2002)Google Scholar
  13. 13.
    Tuytelaars, T., Van Gool, L.: Matching widely separated views based on affine invariant regions. IJCV 59(1), 61–85 (2004)CrossRefGoogle Scholar
  14. 14.
    Mikolajczyk, K., Zisserman, A., Schmid, C.: Shape recognition with edge-based features. In: Proc. BMVC, vol. 2, pp. 779–788 (2003)Google Scholar
  15. 15.
    Torr, P.H.S.: Geometric motion segmentation and model selection. Phil. Trans. R. Soc. Lond. A 356, 1321–1340 (1998)MATHCrossRefMathSciNetGoogle Scholar
  16. 16.
    Hartley, R., Zisserman, A.: Multiple view geometry in computer vision, 2nd edn. Cambridge University Press, Cambridge (2003)Google Scholar
  17. 17.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with application to image analysis and automated cartography. ACM Graphics and Image Processing 24(6), 381–395 (1981)MathSciNetGoogle Scholar
  18. 18.
    Mikolajczyk, K., Schmid, C.: Indexing based on scale invariant interest points. In: Proc. ICCV, vol. 1, pp. 525–531 (2001)Google Scholar
  19. 19.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. In: Proc. CVPR, pp. 257–264 (2003)Google Scholar
  20. 20.
    Moller, T., Haines, E.: Real-time rendering, 2nd edn. A.K.Peters (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Naoyuki Ichimura
    • 1
  1. 1.National Institute of Advanced Industrial Science and Technology (AIST)Tsukuba, IbarakiJapan

Personalised recommendations