Skip to main content

Advertisement

Log in

An adaptive focus-of-attention model for video surveillance and monitoring

Machine Vision and Applications Aims and scope Submit manuscript

Abstract

In current video surveillance systems, commercial pan/tilt/zoom (PTZ) cameras typically provide naive (or no) automatic scanning functionality to move a camera across its complete viewable field. However, the lack of scene-specific information inherently handicaps these scanning algorithms. We address this issue by automatically building an adaptive, focus-of-attention, scene-specific model using standard PTZ camera hardware. The adaptive model is constructed by first detecting local human activity (i.e., any translating object with a specific temporal signature) at discrete locations across a PTZ camera’s entire viewable field. The temporal signature of translating objects is extracted using motion history images (MHIs) and an original, efficient algorithm based on an iterative candidacy-classification-reduction process to separate the target motion from noise. The target motion at each location is then quantified and employed in the construction of a global activity map for the camera. We additionally present four new camera scanning algorithms which exploit this activity map to maximize a PTZ camera’s opportunity of observing human activity within the camera’s overall field of view. We expect that these efficient and effective algorithms are implementable within current commercial camera systems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  1. Aggarwal, J., Cai, Q.: Human motion analysis: a review. In: Nonrigid and Articulated Motion Workshop, pp. 90–102 (1997)

  2. Bobick A., DavisJ. (2001) The recognition of human movement using temporal templates. IEEE Trans. Patt. Anal. Mach. Intell. 23(3): 257–267

    Article  Google Scholar 

  3. Bradski, G., Davis, J.: Motion segmentation and pose recognition with motion history gradients. In: Proceedings of Workshop on Applications of Computer Vis. (2000)

  4. Cedras C., Shah M. (1995) Motion-based recognition: a survey. Image Vis. Comp. 13(2): 129–155

    Article  Google Scholar 

  5. Chang I., Huang C. (2000) The model-based human body motion analysis system. Image Vis. Comp. 18(14): 1067–1083

    Article  Google Scholar 

  6. Dalal, N., Triggs, B.: An effective pedestrian detector based on evaluating histograms of oriented image gradients in a grid. In: Proceedings of Computer Vision and Pattern Recognition (2005)

  7. Davis, J.: Visual categorization of children and adult walking styles. In: Proceedings of international conference on Audio- and Video-based Biometric Person Authentication, pp. 295–300 (2001)

  8. Davis, J., Keck, M.: Modeling behavior trends and detecting event anomalies using seasonal Kalman filters. In: Proceedings of Workshop on Human Activity Recognition and Modeling (2005)

  9. Davis, J., Keck, M.: A two-stage template approach to person detection in thermal imagery. In: Proceedings of Workshop on Applications of Computer Vis. (2005)

  10. Davis, J., Sharma, V.: Background-subtraction in thermal imagery using contour saliency. Int. J. Comp. Vis. (to appear)

  11. Gavrila D. (1999) The visual analysis of human movement: a survey. Comput. Vis. Image Understand. 73(1): 82–98

    Article  Google Scholar 

  12. Gavrila, D.: Pedestrian detection from a moving vehicle. In: Proceedings of European Conference Computer Vision, pp. 37–49 (2000)

  13. Itti, L., Baldi, P.: A principled approach to detecting surprising events in video. In: Proceedings of Computer Vision and Pattern Recognition, pp. 631–637 (2005)

  14. Javed, O., Shafique, K., Shah, M.: A hierarchical approach to robust background subtraction using color and gradient information. In: Workshop on Motion and Video Computing, pp. 22–27, (2002)

  15. Johnson, N., Hogg, D.: Learning the distribution of object trajectories for event recognition. In: British Machine Vision Conference, pp. 583–592 (1995)

  16. Lipton, A., Fujiyoshi, H., Patil, R.: Moving target classification and tracking from real-time video. In: Proceedings of Workshop on Applications of Computer Vision, pp. 8–14 (1998)

  17. Little J., Boyd J. (1998) Recognizing people by their gait: the shape of motion. Videre 1(2): 2–32

    Google Scholar 

  18. Liu, Y., Collins, R., Tsin, Y.: Gait sequence analysis using frieze patterns. In: Proceedings of European Conference Computer Vision, pp. 657–671 (2002)

  19. Makris, D., Ellis, T.: Automatic learning of an activity-based semantic scene model. In: Advanced Video and Signal Based Surveillance, pp. 183–188 (2003)

  20. Minka, T., Picard, R.: Interactive learning using a “society of models”. In: Proceedings of Computer Vision Pattern Recognition, pp. 447–452 (1996)

  21. Mitchell T. (1997) Machine Learning. McGraw-Hill, New York

    MATH  Google Scholar 

  22. Niyogi, S., Adelson, E.: Analyzing and recognizing walking figures in XYT. In: Proceedings of Computer Vision and Pattern Recognition, pp. 469–474 (1994)

  23. Remote Reality, http://www.remotereality.com/vtprod/index. html

  24. Oren, M., Papageorgiou, C., Sinha, P., Osuma, E., Poggio, T.: Pedestrian detection using wavelet templates. In: Proceedings of Computer Vision and Pattern Recognition, pp. 193–199 (1997)

  25. Pelco Spectra III SE. http://www.pelco.com/products/default. aspx? id=337

  26. Van Rijsbergen C. (1979) Information Retrieval. Department of Computer Science, 2nd edn. University of Glasgow

    Google Scholar 

  27. Rubner Y., Guibas L., Tomassi C. (2000) The earth mover’s distance as a metric for image retrieval. Int. J. Comp. Vis. 40(2): 99–121

    Article  Google Scholar 

  28. Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: Proceedings of Computer Vision and Pattern Recognition, pp. 246–252 (1999)

  29. Strat, T., Fischler, M.: Context-based vision: recognizing objects using information from both 2-d and 3-d imagery. In: IEEE Transaction Pattern Analysis and Machine Intelligence, pp. 1050–1065 (1991)

  30. Viola, P., Jones, M., Snow, D.: Detecting pedestrians using patterns of motion and appearance. In: Proceedings of International Conference Computer Vision, pp. 734–741 (2003)

  31. Wang L., Hu W., Tan T. (2003) Recent developments in human motion analysis. Pattern Recogn. 36, 585–601

    Article  Google Scholar 

  32. Wren C., Azarbayejani A., Darrell T., Pentland A. (1997) Pfinder: real-time tracking of the human body. IEEE Transaction on Pattern Analysis and Machine Intellignece 19(7): 780–785

    Article  Google Scholar 

  33. Zhaozheng, Y., Robert, C.: Moving object localization in thermal imagery by forward-backward MHI. In: IEEE International Workshop on Object Tracking and Classification. Beyond the Vision Spectrum (2006)

  34. Zhao, T., Nevatia, R.: Stochastic human segmentation from a static camera. In: Workshop on Motion and Video Computing, pp. 9–14 (2002)

  35. Zhou, X., Collins, R., Kanade, T., Metes, P.: A master–slave system to acquire biometric imagery of humans at distance. International Workshop on Video Surveillance, pp. 113–120 (2003)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to James W. Davis.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Davis, J.W., Morison, A.M. & Woods, D.D. An adaptive focus-of-attention model for video surveillance and monitoring. Machine Vision and Applications 18, 41–64 (2007). https://doi.org/10.1007/s00138-006-0047-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-006-0047-x

Keywords

Navigation