Sorting Atomic Activities for Discovering Spatio-temporal Patterns in Dynamic Scenes

  • Gloria Zen
  • Elisa Ricci
  • Stefano Messelodi
  • Nicu Sebe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6978)

Abstract

We present a novel non-object centric approach for discovering activity patterns in dynamic scenes. We build on previous works on video scene understanding. We first compute simple visual cues and individuate elementary activities. Then we divide the video into clips, compute clip histograms and cluster them to discover spatio-temporal patterns. A recently proposed clustering algorithm, which uses as objective function the Earth Mover’s Distance (EMD), is adopted. In this way the similarity among elementary activities is taken into account. This paper presents three crucial improvements with respect to previous works: (i) we consider a variant of EMD with a robust ground distance, (ii) clips are represented with circular histograms and an optimal bin order, reflecting the atomic activities’similarity, is automatically computed, (iii) the temporal dynamics of elementary activities is considered when clustering clips. Experimental results on publicly available datasets show that our method compares favorably with state-of-the-art approaches.

Keywords

Dynamic scene understanding Earth Mover’s Distance Linear Programming Dynamic Time Warping Traveling Salesman Problem 

References

  1. 1.
    Wang, X., Tieu, K., Grimson, W.E.L.: Learning semantic scene models by trajectory analysis. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 110–123. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Zelniker, E.E., Gong, S.G., Xiang, T.: Global Abnormal Detection Using a Network of CCTV Cameras. In: Workshop on Visual Surveillance (2008)Google Scholar
  3. 3.
    Hospedales, T., Gong, S., Xiang, T.: A Markov Clustering Topic Model for Mining Behaviour in Video. In: IEEE International Conference on Computer Vision, ICCV (2009)Google Scholar
  4. 4.
    Varadarajan, J., Emonet, R., Odobez, J.-M.: Probabilistic Latent Sequential Motifs: Discovering temporal activity patterns in video scenes. In: British Machine Vision Conference, BMVC (2010)Google Scholar
  5. 5.
    Kuettel, D., Breitenstein, M.D., Van Gool, L., Ferrari, V.: What’s going on? Discovering Spatio-Temporal Dependencies in Dynamic Scenes. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2010)Google Scholar
  6. 6.
    Zen, G., Ricci, E.: Earth Mover’s Prototypes: a Convex Learning Approach for Discovering Activity Patterns in Dynamic Scenes. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2011)Google Scholar
  7. 7.
    Rubner, Y., Tomasi, C., Guibas, L.J.: The Earth Mover’s Distance as a Metric for Image Retrieval. International Journal of Computer Vision (IJCV) 40(2), 99–121 (2000)CrossRefMATHGoogle Scholar
  8. 8.
    Ling, H., Okada, K.: An efficient earth mover’s distance algorithm for robust histogram comparison. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 29(5), 840–853 (2006)CrossRefGoogle Scholar
  9. 9.
    Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR (1999)Google Scholar
  10. 10.
    Lawler, E.L., Lenstra, J.K., Rinnooy Kan, A.H.G., Shmoys, D.B.: The Traveling Salesman Problem. Wiley, New York (1985)MATHGoogle Scholar
  11. 11.
    Christofides, N.: Worst-case analysis of a new heuristic for the travelling salesman problem. Technical Report 388, Graduate School of Industrial Administration, Carnegie-Mellon University, Pittsburgh, PA (1976)Google Scholar
  12. 12.
    Pele, O., Werman, M.: A linear time histogram metric for improved SIFT matching. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 495–508. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  13. 13.
    Pele, O., Werman, M.: Fast and robust earth mover’s distances. In: IEEE International Conference on Computer Vision, ICCV (2009)Google Scholar
  14. 14.
    Rabiner, L., Juang, B.: Fundamentals of Speech Recognition. Prentice-Hall, Englewood Cliffs (1993)MATHGoogle Scholar
  15. 15.
    Li, J., Gong, S., Xiang, T.: Global Behaviour Inference using Probabilistic Latent Semantic Analysis. In: British Machine Vision Conference, BMVC (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Gloria Zen
    • 1
  • Elisa Ricci
    • 2
  • Stefano Messelodi
    • 3
  • Nicu Sebe
    • 1
  1. 1.Department of Information Engineering and Computer ScienceUniversità di TrentoItaly
  2. 2.Department of Electronic and Information EngineeringUniversità di PerugiaItaly
  3. 3.Fondazione Bruno KesslerTrentoItaly

Personalised recommendations