Statistical Analysis of Visual Attentional Patterns for Video Surveillance

  • Giorgio Roffo
  • Marco Cristani
  • Frank Pollick
  • Cristina Segalin
  • Vittorio Murino
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8259)

Abstract

We show that the way people observe video sequences, other than what they observe, is important for the understanding and the prediction of human activities. In this study, we consider 36 surveillance videos, organized in four categories (confront, nothing, fight, play): the videos are observed by 19 people, ten of them are experienced operators and the other nine are novices, and the gaze trajectories of both populations are recorded by an eye tracking device. Due to the proved superior ability of experienced operators in predicting violence in surveillance footage, our aim is to distinguish the two classes of people, highlighting in which respect expert operators differ from novices. Extracting spatio-temporal features from the eye tracking data, and training standard machine learning classifiers, we are able to discriminate the two groups of subjects with an average accuracy of 80.26%. The idea is that expert operators are more focused on few regions of the scene, sampling them with high frequency and low predictability. This can be thought as a first step toward the advanced automated analysis of video surveillance footage, where machines imitate as best as possible the attentive mechanisms of humans.

Keywords

surveillance gaze control eye movement analysis activity recognition eye tracking 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bahill, A.T., Clark, M.R., Stark, L.: The main sequence, a tool for studying human eye movements. Math. Biosci. (2) (1975)Google Scholar
  2. 2.
    Blechko, A., Darker, I., Gale, A.: Skills in detecting gun carrying from CCTV. In: International Carnahan Conference on Security Technology (2008)Google Scholar
  3. 3.
    Cristani, M., Murino, V., Vinciarelli, A.: Socially intelligent surveillance and monitoring: Analysing social dimensions of physical space. In: CVPRW 2010, pp. 51–58 (2010)Google Scholar
  4. 4.
    Cristani, M., Raghavendra, R., Del Bue, A., Murino, V.: Human behavior analysis in video surveillance: A social signal processing perspective. Neurocomputing 100, 86–97 (2013)CrossRefGoogle Scholar
  5. 5.
    Hales, G., Lewis, C., Silverstone, D.: Gun Crime: The Market in and Use of Illegal Firearms. Findings (Great Britain. Home Office. Research, Development and Statistics Directorate). Home Office (2006)Google Scholar
  6. 6.
    Henderson, J.M., Weeks, P.A., Hollingworth, A.: Multi-feature object trajectory clustering for video analysis. IEEE Transactions on Circuits and Systems for Video Technology 18(11), 1555–1564 (2008)CrossRefGoogle Scholar
  7. 7.
    Ji, R., Sun, X., Yao, H.: What are we looking for: Towards statistical modeling of saccadic eye movements and visual saliency. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, vol. (3), pp. 1552–1559 (2012)Google Scholar
  8. 8.
    Kasarskis, P., Stehwien, J., Hickox, J., Aretz, A., Wickens, C.: Comparison of expert and novice scan behaviors during vfr flight. In: Proceedings of the 11th International Symposium on Aviation Psychology (2001)Google Scholar
  9. 9.
    Land, M.F., Hayhoe, M.: In what ways do eye movements contribute to everyday activities? Vision research 41(25-26), 3559–3565 (2001)CrossRefGoogle Scholar
  10. 10.
    Land, M.F., Lee, D.N.: Where we look when we steer. Nature 369, 742–744 (1994)CrossRefGoogle Scholar
  11. 11.
    Law, B., Atkins, M.S., Kirkpatrick, A.E., Lomax, A.J.: Eye gaze patterns differentiate novice and experts in a virtual laparoscopic surgery training environment, pp. 41–48 (2004)Google Scholar
  12. 12.
    Petrini, K., McAleer, P., Neary, C., Gillard, J., Pollick, F.E.: Experience in judging intent to harm modulates parahippocampal activity: an fmri study with experienced cctv operators. In: European Conference on Visual Perception (2012)Google Scholar
  13. 13.
    Pratt, J.: Visual fixation offsets affect both the initiation and the kinematic features of saccades. Experimental Brain Research 118(1), 135–138 (1998)CrossRefGoogle Scholar
  14. 14.
    Rayner, K.: Eye movements in reading and information processing: 20 years of research.. Psychological bulletin 124(3), 372–422 (1998)CrossRefGoogle Scholar
  15. 15.
    Torralba, A.: Modeling global scene factors in attention. Journal of the Optical Society of America. A, Optics, image science, and vision 20(5), 1407–1418 (2003)CrossRefGoogle Scholar
  16. 16.
    Torralba, A., Castelhano, M.S., Oliva, A., Henderson, J.M.: Contextual guidance of eye movements and attention in real-world scenes: the role of global features in object search. Psychological Review 113 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Giorgio Roffo
    • 1
  • Marco Cristani
    • 1
    • 2
  • Frank Pollick
    • 3
  • Cristina Segalin
    • 1
  • Vittorio Murino
    • 2
    • 1
  1. 1.Department of Computer ScienceUniversity of VeronaItaly
  2. 2.Pattern Analysis and Computer Vision Dept.Istituto Italiano di TecnologiaItaly
  3. 3.School of PsychologyUniversity of GlasgowUK

Personalised recommendations