Open Access

Robust Real-Time Tracking for Visual Surveillance

  • David Thirde1Email author,
  • Mark Borg1,
  • Josep Aguilera2,
  • Horst Wildenauer2,
  • James Ferryman1 and
  • Martin Kampel2
EURASIP Journal on Advances in Signal Processing20062007:096568

https://doi.org/10.1155/2007/96568

Received: 21 October 2005

Accepted: 18 May 2006

Published: 5 September 2006

Abstract

This paper describes a real-time multi-camera surveillance system that can be applied to a range of application domains. This integrated system is designed to observe crowded scenes and has mechanisms to improve tracking of objects that are in close proximity. The four component modules described in this paper are (i) motion detection using a layered background model, (ii) object tracking based on local appearance, (iii) hierarchical object recognition, and (iv) fused multisensor object tracking using multiple features and geometric constraints. This integrated approach to complex scene tracking is validated against a number of representative real-world scenarios to show that robust, real-time analysis can be performed.

Keywords

Object RecognitionBackground ModelGeometric ConstraintMotion DetectionObject Tracking

[123456789101112131415161718192021222324]

Authors’ Affiliations

(1)
School of Systems Engineering, Computational Vision Group, The University of Reading, Reading, UK
(2)
Computer Science Department, Pattern Recognition and Image Processing Group, Vienna University of Technology, Vienna, Austria

References

  1. Bar-Shalom Y, Li XR: Multitarget Multisensor Tracking: Principles and Techniques. YBS Publishing, Storrs, Conn, USA; 1995.Google Scholar
  2. Thirde D, Borg M, Ferryman J, et al.: Visual surveillance for aircraft activity monitoring. Proceedings of the 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS '05), October 2005, Beijing, China 255-262.Google Scholar
  3. Sullivan GD: Visual interpretation of known objects in constrained scenes. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 1992,337(1281):361-370. 10.1098/rstb.1992.0114View ArticleGoogle Scholar
  4. Doermann D, Mihalcik D: Tools and techniques for video performance evaluation. Proceedings of the 15th International Conference on Pattern Recognition (ICPR '00), September 2000, Barcelona, Spain 167-170.View ArticleGoogle Scholar
  5. Jabri S, Duric Z, Wechsler H, Rosenfeld A: Detection and location of people in video images using adaptive fusion of color and edge information. Proceedings of the IEEE/IAPR 15th International Conference on Pattern Recognition (ICPR '00), September 2000, Barcelona, Spain 4: 4627-4631.Google Scholar
  6. Wren CR, Azarbayejani A, Darrell T, Pentland AP: Pfinder: real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence 1997,19(7):780-785. 10.1109/34.598236View ArticleGoogle Scholar
  7. Shi J, Tomasi C: Good features to track. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '94), June 1994, Seattle, Wash, USA 593-600.Google Scholar
  8. Collins R, Lipton A, Kanade T, et al.: A system for videosurveillance and monitoring: VSAM final report. In Tech. Rep. CMU-RI-TR-00-12. Robotics Institute, Carnegie Mellon University, Pittsburgh, Pa, USA; May 2000.Google Scholar
  9. Stauffer C, Grimson WEL: Adaptive background mixture models for real-time tracking. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '99), June 1999, Fort Collins, Colo, USA 2: 246-252.View ArticleGoogle Scholar
  10. Xu G, Zhang Z: Epipolar Geometry in Stereo, Motion and Object Recognition: A Unified Approach. Kluwer Academic, Dordrecht, The Netherlands; 1996.View ArticleMATHGoogle Scholar
  11. Siebel NT, Maybank SJ: Fusion of multiple tracking algorithms for robust people tracking. Proceedings of the 7th European Conference on Computer Vision (ECCV '02), May 2002, Copenhagen, Denmark 373-387.Google Scholar
  12. Ferryman J, Worrall AD, Maybank SJ: Learning enhanced 3D models for vehicle tracking. Proceedings of the British Machine Vision Conference, September 1998, Southampton, UK 873-882.Google Scholar
  13. Black J, Ellis T: Multi camera image measurement and correspondence. Measurement - Journal of the International Measurement Confederation 2002,35(1):61-71.Google Scholar
  14. Xu M, Orwell J, Jones G: Tracking football players with multiple cameras. Proceedings of the IEEE International Conference on Image Processing (ICIP '04), October 2004, Suntec City, Singapore 2: 2909-2912.Google Scholar
  15. Aguilera J, Wildenauer H, Kampel M, Borg M, Thirde D, Ferryman J: Evaluation of motion segmentation quality for aircraft activity surveillance. Proceedings of the 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS '05), October 2005, Beijing, China 293-300.Google Scholar
  16. Correia P, Pereira F: Objective evaluation of relative segmentation quality. Proceedings of the IEEE International Conference on Image Processing (ICIP '00), September 2000, Vancouver, British Columbia, Canada 1: 308-311.Google Scholar
  17. Ellis T: Performance metrics and methods for tracking in surveillance. Proceedings of the 3rd IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS '02), June 2002, Copenhagen, Denmark 26-31.Google Scholar
  18. Erdem CE, Sankur B: Performance evaluation metrics for object-based video segmentation. Proceedings of the 10th European Signal Processing Conference (EUSIPCO '00), September 2000, Tampere, Finland 917-920.Google Scholar
  19. Schlögl T, Beleznai C, Winter M, Bischof H: Performance evaluation metrics for motion detection and tracking. Proceedings of the International Conference on Pattern Recognition (ICPR '04), August 2004, Cambridge, UK 4: 519-522.Google Scholar
  20. Villegas P, Marichal X: Perceptually-weighted evaluation criteria for segmentation masks in video sequences. IEEE Transactions on Image Processing 2004,13(8):1092-1103. 10.1109/TIP.2004.828433View ArticleGoogle Scholar
  21. Mezaris V, Kompatsiaris I, Strintzis MG: Still image objective segmentation evaluation using ground truth. Proceedings of the 5th COST 276 Workshop on Information and Knowledge Management for Integrated Media Communication, October 2003, Prague, Czech Republic 9-14.Google Scholar
  22. Cavallaro A, Gelasca ED, Ebrahimi T: Objective evaluation of segmentation quality using spatio-temporal context. Proceedings of the IEEE International Conference on Image Processing (ICIP '02), September 2002, Rochester, NY, USA 3: 301-304.Google Scholar
  23. Black J, Ellis T, Rosin P: A Novel method for video tracking performance evaluation. Proceedings of Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS '03), October 2003, Nice, France 125-132.Google Scholar
  24. Needham CJ, Boyle RD: Performance evaluation metrics and statistics for positional tracker evaluation. Proceedings of the 3rd International Conference on Computer Vision Systems (ICVS '03), April 2003, Graz, Austria 278-289.Google Scholar

Copyright

© David Thirde et al. 2007

Advertisement