Advertisement

Detecting Interesting Events Using Unsupervised Density Ratio Estimation

  • Yuichi Ito
  • Kris M. Kitani
  • James A. Bagnell
  • Martial Hebert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7585)

Abstract

Generating meaningful digests of videos by extracting interesting frames remains a difficult task. In this paper, we define interesting events as unusual events which occur rarely in the entire video and we propose a novel interesting event summarization framework based on the technique of density ratio estimation recently introduced in machine learning. Our proposed framework is unsupervised and it can be applied to general video sources, including videos from moving cameras. We evaluated the proposed approach on a publicly available dataset in the context of anomalous crowd behavior and with a challenging personal video dataset. We demonstrated competitive performance both in accuracy relative to human annotation and computation time.

Keywords

Video Summarization Density Ratio Estimation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
    Sugiyama, M., Yamada, M., von Bunaud, P., Suzuki, T., Kanamori, T., Kawanabe, M.: Direct density-ratio estimation with dimensionality reduction via least-squares hetero-distributional subspace search. Neural Networks 24 (2011)Google Scholar
  3. 3.
    Pritch, Y., Rav-Acha, A., Peleg, S.: Nonchronological video synopsis and indexing. IEEE Trans. Pattern Anal. Mach. Intell. 30, 1971–1984 (2008)CrossRefGoogle Scholar
  4. 4.
    Kosmopoulos, D.I., Doulamis, N.D., Voulodimos, A.S.: Bayesian filter based behavior recognition in workflows allowing for user feedback. Computer Vision and Image Understanding 116, 422–434 (2012)CrossRefGoogle Scholar
  5. 5.
    Li, Y., Lee, S.H., Yeh, C.H., Kuo, C.C.J.: Techniques for movie content analysis and skimming. Signal Processing Magazine 23, 79–89 (2006)zbMATHCrossRefGoogle Scholar
  6. 6.
    Nam, J., Tewfik, A.H.: Video abstract of video. In: Proc. IEEE 3rd Workshop Multimedia Signal Processing, pp. 117–122 (1999)Google Scholar
  7. 7.
    Jolic, N., Petrovic, N., Huang, T.: Scene generative models for adaptive video fast forward. In: Proc. ICIP (2003)Google Scholar
  8. 8.
    Xiong, Z., Radhakrishnan, R., Divakaran, A.: Generation of sports highlights using motion activity in combination with a common audio feature extraction framework. In: Proc. ICIP, vol. 1, pp. I–5–I–8 (2003)Google Scholar
  9. 9.
    Mehran, M.S.R., Oyama, A.: Abnormal crowd behavior detection using social force model. In: Proc. CVPR (2009)Google Scholar
  10. 10.
    Wu, S., Moore, B., Shah, M.: Chaotic invariants of lagrangian particle trajectories for anomaly detection in crowded scenes. In: Proc. CVPR (2010)Google Scholar
  11. 11.
    Seo, H.J., Milanfar, P.: Static and space-time visual saliency detection by self-resemblance. Journal of Vision (2009)Google Scholar
  12. 12.
    Cong, Y., Yuan, J., Liu, J.: Sparse reconstruction cost for abnormal event detection. In: Proc. CVPR (2011)Google Scholar
  13. 13.
    Piciarelli, C., Micheloni, C., Foresti, G.: Trajectory-based anomalous event detection. IEEE Transaction on Circuits and Systems for Video Technology 18 (2008)Google Scholar
  14. 14.
    Zhang, D., Gatica-Perez, D., Bengio, S., McCowan, I.: Semi-supervised adapted hmms for unusual event detection. In: Proc. CVPR (2005)Google Scholar
  15. 15.
    Zhao, M., Saligrama, V.: Anomaly detection with score functions based on nearest neighbor graphs. In: Proc. NIPS (2009)Google Scholar
  16. 16.
    Matsugu, M., Yamanaka, M., Sugiyama, M.: Detection of activities and events without explicit categorization. In: Proc. ICCV Workshop (2011)Google Scholar
  17. 17.
    Platt, J.C.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Advances in Large Margin Classifiers (1999)Google Scholar
  18. 18.
    Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: Proc. CVPR (2008)Google Scholar
  19. 19.
    Dalal, N., Triggs, B., Schmid, C.: Human Detection Using Oriented Histograms of Flow and Appearance. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 428–441. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  20. 20.
    Scholkopf, B., Williamson, R., Smola, A., Taylor, J.S., Platt, J.C.: Support vector method for novelty detection. In: Proc. NIPS (2000)Google Scholar
  21. 21.
    Canu, S., Grandvalet, Y., Guigue, V., Rakotomamonjy, A.: Svm and kernel methods matlab toolbox. INSA de Rouen, Rouen, France (2005)Google Scholar
  22. 22.
  23. 23.

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yuichi Ito
    • 1
  • Kris M. Kitani
    • 2
  • James A. Bagnell
    • 2
  • Martial Hebert
    • 2
  1. 1.Nikon CorporationShinagawaJapan
  2. 2.Carnegie Mellon UniversityPittsburghUSA

Personalised recommendations