Unsupervised Discovery, Modeling, and Analysis of Long Term Activities

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6962)


This work proposes a complete framework for human activity discovery, modeling, and recognition using videos. The framework uses trajectory information as input and goes up to video interpretation. The work reduces the gap between low-level vision information and semantic interpretation, by building an intermediate layer composed of Primitive Events. The proposed representation for primitive events aims at capturing meaningful motions (actions) over the scene with the advantage of being learned in an unsupervised manner. We propose the use of Primitive Events as descriptors to discover, model, and recognize activities automatically. The activity discovery is performed using only real tracking data. Semantics are added to the discovered activities (e.g., “Preparing Meal”, “Eating”) and the recognition of activities is performed with new datasets.


Activity Space Term Activity Action Segment Candidate Activity Scene Model 
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.
    Antonini, G., Thiran, J.: Trajectories clustering in ICA space: an application to automatic counting of pedestrians in video sequences. In: ACIVS 2004. Proc. Intl. Soc. Mag. Reson. Med. IEEE, Los Alamitos (2004)Google Scholar
  2. 2.
    Bobick, A.F., Wilson, A.D.: A state-based approach to the representation and recognition of gesture. IEEE Trans. Pattern Anal. Mach. Intell. 19(12), 1325–1337 (1997)CrossRefGoogle Scholar
  3. 3.
    Bouguet, J.Y.: Pyramidal implementation of the Lucas Kanade feature tracker description of the algorithm (2000)Google Scholar
  4. 4.
    Georgescu, B., Shimshoni, I., Meer, P.: Mean shift based clustering in high dimensions: A texture classification example. In: 9th ICCV, pp. 456–463 (2003)Google Scholar
  5. 5.
    Gong, S., Xiang, T.: Recognition of group activities using dynamic probabilistic networks. In: ICC 2003, pp. 742–749 (2003)Google Scholar
  6. 6.
    Hamid, R., Maddi, S., Johnson, A., Bobick, A., Essa, I., Isbell, C.: A novel sequence representation for unsupervised analysis of human activities. A.I. Journal (2009)Google Scholar
  7. 7.
    Haritaoglu, I., Harwood, D., Davis, L.S.: W4: Real-time surveillance of people and their activities. TPAMI 22(8), 809–830 (2000)CrossRefGoogle Scholar
  8. 8.
    Hu, W., Xiao, X., Fu, Z., Xie, D.: A system for learning statistical motion patterns. TPAMI 28(9), 1450–1464 (2006)CrossRefGoogle Scholar
  9. 9.
    Johnson, N., Hogg, D.: Learning the distribution of object trajectories for event recognition. In: BMVC 1995, Surrey, UK, pp. 583–592 (1995)Google Scholar
  10. 10.
    Khalid, S., Naftel, A.: Classifying spatiotemporal object trajectories using unsupervised learning of basis function coefficients. In: VSSN 2005: Proc. of Intl Workshop on Video Surveillance & Sensor Networks (2005)Google Scholar
  11. 11.
    Laptev, I., Lindeberg, T.: Space-time interest points. In: ICCV, pp. 432–439 (2003)Google Scholar
  12. 12.
    Morris, B.T., Trivedi, M.M.: Learning and classification of trajectories in dynamic scenes: A general framework for live video analysis. In: AVSS 2008 (2008)Google Scholar
  13. 13.
    Owens, J., Hunter, A.: Application of the self-organizing map to trajectory classification. In: VS 2000: Proc. of the Third IEEE Int. Workshop on Visual Surveillance (2000)Google Scholar
  14. 14.
    Piciarelli, C., Foresti, G.L.: On-line trajectory clustering for anomalous events detection. Pattern Recogn. Lett. 27(15), 1835–1842 (2006)CrossRefGoogle Scholar
  15. 15.
    Porikli, F.: Learning object trajectory patterns by spectral clustering. In: IEEE International Conference on Multimedia and Expo., ICME 2004, vol. 2, pp. 1171–1174 (2004)Google Scholar
  16. 16.
    Romdhane, R., Mulin, E., Derreumeaux, A., Zouba, N., Piano, J., Lee, L., Leroi, I., Mallea, P., David, R., Thonnat, M., Bremond, F., Robert, P.: Automatic video monitoring system for assessment of alzheimer’s disease symptoms. JNHA - The Journal of Nutrition, Health and Aging Ms. No. JNHA-D-11-00004R1 (2011)Google Scholar
  17. 17.
    Calderara, S., Cucchiara, R., Prati, A.: Detection of abnormal behaviors using a mixture of von mises distributions. In: IEEE AVSS (2007)Google Scholar
  18. 18.
    Shi, J., Tomasi, C.: Good features to track. In: IEEE CVPR 1994, p. 593–600 (1994)Google Scholar
  19. 19.
    Zouba, N., Bremond, F., Thonnat, M.: Multisensor fusion for monitoring elderly activities at home. In: AVSS 2009, Genoa, Italy (September 2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Pulsar, Inria - Sophia AntipolisFrance

Personalised recommendations