Advertisement

Recognizing Complex Events Using Large Margin Joint Low-Level Event Model

  • Hamid Izadinia
  • Mubarak Shah
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7575)

Abstract

In this paper we address the challenging problem of complex event recognition by using low-level events. In this problem, each complex event is captured by a long video in which several low-level events happen. The dataset contains several videos and due to the large number of videos and complexity of the events, the available annotation for the low-level events is very noisy which makes the detection task even more challenging. To tackle these problems we model the joint relationship between the low-level events in a graph where we consider a node for each low-level event and whenever there is a correlation between two low-level events the graph has an edge between the corresponding nodes. In addition, for decreasing the effect of weak and/or irrelevant low-level event detectors we consider the presence/absence of low-level events as hidden variables and learn a discriminative model by using latent SVM formulation. Using our learned model for the complex event recognition, we can also apply it for improving the detection of the low-level events in video clips which enables us to discover a conceptual description of the video. Thus our model can do complex event recognition and explain a video in terms of low-level events in a single framework. We have evaluated our proposed method over the most challenging multimedia event detection dataset. The experimental results reveals that the proposed method performs well compared to the baseline method. Further, our results of conceptual description of video shows that our model is learned quite well to handle the noisy annotation and surpass the low-level event detectors which are directly trained on the raw features.

Keywords

Action Recognition Event Recognition Training Video Birthday Party MFCC Feature 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local svm approach. In: ICPR (2004)Google Scholar
  2. 2.
    Rodriguez, M., Ahmed, J., Shah, M.: Action mach a spatio-temporal maximum average correlation height filter for action recognition. In: CVPR (2008)Google Scholar
  3. 3.
  4. 4.
    Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., Serre, T.: HMDB: a large video database for human motion recognition. In: ICCV (2011)Google Scholar
  5. 5.
    Le, Q., Zou, W., Yeung, S., Ng, A.: Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis. In: CVPR (2011)Google Scholar
  6. 6.
    Wang, H., Kläser, A., Schmid, C., Cheng-Lin, L.: Action recognition by dense trajectories. In: CVPR (2011)Google Scholar
  7. 7.
    Jiang, Y.G., Yang, J., Ngo, C.W., Hauptmann, A.G.: Representations of keypoint-based semantic concept detection: A comprehensive study. IEEE Trans. Multimedia 12(1), 42–53 (2010)CrossRefGoogle Scholar
  8. 8.
    Merler, M., Huang, B., Xie, L., Hua, G., Natsev, A.: Semantic model vectors for complex video event recognition. IEEE Trans. Multimedia 14(1), 88–101 (2012)CrossRefGoogle Scholar
  9. 9.
    Natarajan, P., et al.: Bbn viser trecvid 2011 multimedia event detection system. In: NIST TRECVID Workshop (2011)Google Scholar
  10. 10.
    Farhadi, A., Endres, I., Hoiem, D., Forsyth, D.: Describing objects by their attributes. In: CVPR (2009)Google Scholar
  11. 11.
    Ferrari, V., Zisserman, A.: Learning visual attributes. In: NIPS (2007)Google Scholar
  12. 12.
    Wang, Y., Mori, G.: A Discriminative Latent Model of Object Classes and Attributes. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 155–168. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Lampert, C., Nickisch, H., Harmeling, S.: Learning to detect unseen object classes by between-class attribute transfer. In: CVPR (2009)Google Scholar
  14. 14.
    Siddiquie, B., Feris, R., Davis, L.: Image ranking and retrieval based on multi-attribute queries. In: CVPR (2011)Google Scholar
  15. 15.
    Liu, J., Kuipers, B., Savarese, S.: Recognizing human actions by attributes. In: CVPR (2011)Google Scholar
  16. 16.
    Gaidon, A., Harchaoui, Z., Schmid, C.: Actom sequence models for efficient action detection. In: CVPR (2011)Google Scholar
  17. 17.
    Niebles, J.C., Chen, C.-W., Fei-Fei, L.: Modeling Temporal Structure of Decomposable Motion Segments for Activity Classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part II. LNCS, vol. 6312, pp. 392–405. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  18. 18.
    Tang, K., Fei-Fei, L., Koller, D.: Learning latent temporal structure for complex event detection. In: CVPR (2012)Google Scholar
  19. 19.
    Laptev, I.: On space time interest points. IJCV 64 (2005)Google Scholar
  20. 20.
    Dollar, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior recognition via sparse spatio-temporal features. In: IEEE International Workshop on VS-PETS (2005)Google Scholar
  21. 21.
    Oliva, A., Torralba, A.: Modeling the shape of the scene: A holistic representation of the spatial envelope. IJCV 42, 145–175 (2001)MATHCrossRefGoogle Scholar
  22. 22.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60(2) (2004)Google Scholar
  23. 23.
    Rabiner, L., Juang, B.H.: Fundamentals of Speech Recognition. Prentice Hall (1993)Google Scholar
  24. 24.
    Do, T.M.T., Artières, T.: Large margin training for hidden markov models with partially observed states. In: ICML (2009)Google Scholar
  25. 25.
    Trecvid multimedia event detection track (2011), http://www.nist.gov/itl/iad/mig/med11.cfm
  26. 26.
    Everingham, M., Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. IJCV 88, 303–338 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hamid Izadinia
    • 1
  • Mubarak Shah
    • 1
  1. 1.Computer Vision LabUniversity of Central FloridaOrlandoUSA

Personalised recommendations