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Unsupervised Activity Analysis and Monitoring Algorithms for Effective Surveillance Systems

  • Jean-Marc Odobez
  • Cyril Carincotte
  • Rémi Emonet
  • Erwan Jouneau
  • Sofia Zaidenberg
  • Bertrand Ravera
  • Francois Bremond
  • Andrea Grifoni
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7585)

Abstract

In this demonstration, we will show the different modules related to the automatic surveillance prototype developed in the context of the EU VANAHEIM project. Several components will be demonstrated on real data from the Torino metro. First, different unsupervised activity modeling algorithms that capture recurrent activities from long recordings will be illustrated. A contrario, they provide unusuallness measures that can be used to select the most interesting streams to be displayed in control rooms. Second, different scene analysis algorithms will be demonstrated, ranging from left-luggage detection to the automatic identification of groups and their tracking. Third, a set of situationnal reporting methods (flow and count monitoring in escalators and at platforms as well as human presence at lift ) that provide a global view of the activity in the metro station and are displayed on maps or along with analyzed video streams. Finally, an offline activity discovery tool based on long term recordings. All algorithms are integrated into a Video Management Solution using an innovative VideoWall module that will be demonstrated as well.

Keywords

Anomaly Detection Metro Station Group Detection Advance Video Audio Event 
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.

References

  1. 1.
    Emonet, R., Varadarajan, J., Odobez, J.M.: Extracting and locating temporal motifs in video scenes using a hierarchical non parametric bayesian model. In: CVPR (2011)Google Scholar
  2. 2.
    Emonet, R., Varadarajan, J., Odobez, J.M.: Multi-camera Open Space Human Activity Discovery for Anomaly Detection. In: IEEE International Conference on Advanced Video and Signal-Based Surveillancei AVSS (2011)Google Scholar
  3. 3.
    Jouneau, E., Carincotte, C.: Particle-based tracking model for automatic anomaly detection. In: International Conference on Image Processing (2011)Google Scholar
  4. 4.
    Jouneau, E., Carincotte, C.: Mono versus multi-view tracking-based model for automatic scene activity modeling and anomaly detection. In: IEEE Int. Conf. on Advanced Video and Signal-Based Surveillance, AVSS (2011)Google Scholar
  5. 5.
    Lecomte, S., Lengellé, R., Richard, C., Capman, F., Ravera, B.: Abnormal events detection using unsupervised one-class svm - application to audio surveillance and evaluation. In: IEEE Int. Conf. on Advanced Video and Signal-Based Surveillance (2011)Google Scholar
  6. 6.
    Heili, A., Chen, C., Odobez, J.: Detection-based multi-human tracking using a crf model. In: ICCV Workshop Visual Surveillance (2011)Google Scholar
  7. 7.
    Chen, C., Odobez, J.M.: We are not contortionists: coupled adaptive learning for head and body orientation estimation in surveillance video. In: CVPR (2012)Google Scholar
  8. 8.
    Yao, J., Odobez, J.M.: Multi-layer background subtraction based on color and texture. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1–8 (June 2007)Google Scholar
  9. 9.
    Yao, J., Odobez, J.M.: Fast human detection from joint appearance and foreground feature subset covariances. Computer Vision and Image Understanding 115(10), 1414–1426 (2011)CrossRefGoogle Scholar
  10. 10.
    Zaidenberg, S., Boulay, B., Garate, C., Chau, D., Corvee, E., Bremond, F.: Group interaction and group tracking for video-surveillance in underground railway stations. In: Int. Workshop on Behaviour Analysis, ICVS (2011)Google Scholar
  11. 11.
    Zaidenberg, S., Boulay, B., Brémond, F.: A generic framework for video understanding applied to group behavior recognition. In: IEEE Int. Conf. on Advanced Video and Signal-Based Surveillance, AVSS (2012)Google Scholar
  12. 12.
    Descamps, A., Carincotte, C., Gosselin, B.: Person detection for indoor videosurveillance using spatio-temporal integral features. In: Interactive Human Behavior Analysis in Open or Public Spaces Workshop, INTERHUB (2011)Google Scholar
  13. 13.
    Patino, L., Bremond, F., Thonnat, M.: Online learning of activities from video. In: IEEE Int. Conf. on Advanced Video and Signal-Based Surveillance, AVSS (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jean-Marc Odobez
    • 1
  • Cyril Carincotte
    • 2
  • Rémi Emonet
    • 1
  • Erwan Jouneau
    • 2
  • Sofia Zaidenberg
    • 3
  • Bertrand Ravera
    • 4
  • Francois Bremond
    • 3
  • Andrea Grifoni
    • 5
  1. 1.Idiap Research InstituteSwitzerland
  2. 2.MultitelBelgium
  3. 3.INRIAFrance
  4. 4.Thales CommunicationFrance
  5. 5.Thales ItaliaItaly

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