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Video understanding for complex activity recognition

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Abstract

This paper presents a real-time video understanding system which automatically recognises activities occurring in environments observed through video surveillance cameras. Our approach consists in three main stages: Scene Tracking, Coherence Maintenance, and Scene Understanding. The main challenges are to provide a robust tracking process to be able to recognise events in outdoor and in real applications conditions, to allow the monitoring of a large scene through a camera network, and to automatically recognise complex events involving several actors interacting with each others. This approach has been validated for Airport Activity Monitoring in the framework of the European project AVITRACK.

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References

  1. Aguilera, J., Wildernauer, H., Kampel, M., Borg, M., Thirde, D., Ferryman, J.: Evaluation of motion segmentation quality for aircraft activity surveillances. In: Proceedings of IEEE International Workshop on VS-PETS, Beijing (2005)

  2. Allen, J.: Maintaining knowledge about temporal intervals. Commun ACM 26(11), pp. 823–843 (1983)

  3. Avanzi, A., Bremond, F., Thonnat, M.: Tracking multiple individuals for video communication. In: Proceedings of IEEE International Conference on Image Processing Thessaloniki, Greece (2001)

  4. Bar-Shalom, Y., Li, X.: Multitarget-Multisensor Tracking: Principles and Techniques. YBS Publishing (1995)

  5. Black J. and Ellis T. (2002). Multi Camera Image Measurement and Correspondence. Meas J Int Meas Confed 35(1): 61–71

    Google Scholar 

  6. Chleq, N., Thonnat, M.: Real-time image sequence interpretation for video-surveillance applications. In: Proceedings of IEEE International conference on Image Processing, vol 2, pp. 801–804 (1996)

  7. Collins, R., Lipton, A., Kanade, T., Fujiyoshi, H., Duggins, D., Tsin, Y., Tolliver, D., Enomoto, N., Hasegawa, O., Burt, P., Wixson, L.: A system for video surveillance and monitoring. In: Technical report CMU-RI-TR-00-12 (2002)

  8. Cupillard, F., Avanzi, A., Bremond, F., Thonnat, M.: Video understanding for metro surveillance. In: Proceedings of IEEE ICNSC 2004 in the Special Session on Intelligent Transportation Systems Taipei, Taiwan (2004)

  9. Ferryman, J.M., Worrall, A.D., Maybank, S.J.: Learning enhanced 3d models for vehicle tracking. In: Proceedings of the British Machine Vision Conference Nottingham (1998)

  10. Georis, B., Maziere, M., Bremond, F., Thonnat, M.: A video interpretation platform applied to bank agency monitoring. In: Proceedings of IDSS’04—2nd Workshop on Intelligent Distributed Surveillance Systems London, UK (2004)

  11. Ghallab., M.: On chronicles: representation, on-line recognition and learning. In: Proceedings of 5th International Conference on Principles of Knowledge Representation and Reasoning (KR’96) Cambridge, USA (1996)

  12. Horprasert, T., Harwood, D., Davis, L.: A statistical approach for real-time robust background subtraction and shadow detection. In: IEEE ICCV’99 FRAME-RATE Workshop, Kerkyra (1999)

  13. Howell, A., Buxton, H.: Active vision techniques for visually mediated interaction. Image Vis Comput vol 20, pp. 861–871 (2002)

  14. Khoudour, L., Hindmarsh, J., Aubert, D., Velastin, S., Heath, C.: Enhancing security management in public transport using automatic incident detection. In: Sucharov, L., Brebbia, C. (eds.) Urban Transport VII: Proceedings of the 7th International Conference on Urban Transport of the Environment for the 21st Century. WIT Press, pp. 619–628 (2001)

  15. Lo, B., Velastin, S.: Automatic congestion detection system for underground platforms. In: International Symposium on Intelligent Multimedia, Video and Speech Processing, pp. 823–843 (2001)

  16. Piater, J., Richetto, S., Crowley, J.: Event-based activity analysis in live video using a generic object tracker. In: Proceedings of the 3rd IEEE Workshop on Performance Evaluation of Tracking and Surveillance (2002)

  17. Pinhanez, C., Bobick., A.: Human action detection using pnf propagation of temporal constraints. In: M.I.T. Media Laboratory Perceptual Section Report, vol. 423 (1997)

  18. R. Gerber, H.N., Schreiber., H.: Deriving textual descriptions of road traffic queues from video sequences. In: The 15th European Conference on Artificial Intelligence (ECAI’2002) (2002)

  19. Rota, N., Thonnat, M.: Activity recognition from video sequences using declarative models. In: Proceedings of 14th European Conference on Artificial Intelligence (ECAI 2000) Berlin, Germany (2000)

  20. S. Hongeng, F. Bernard, Nevatia, R.: Representation and optimal recognition of human activities. In: IEEE Proceedings of Computer Vision and Pattern Recognition South Carolina, USA (2000)

  21. Shet, V., Harwood, D., David, L.: Vidmap: video monitoring of activity with prolog. In: Proceedings of IEEE International Conference on Advanced Video and Signal based Surveillance, pp. 224–229 (2005)

  22. Shi, J., Tomasi, C.: Good features to track. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 593–600 (1994)

  23. Sullivan G.D. (1992). Visual interpretation of known objects in constrained scenes. In: Phil. Trans. R. Soc. Lon., vol. B 337: 361–370

    Article  Google Scholar 

  24. Thirde, D., Borg, M., Aguilera, J., Ferryman, J., Baker, K., Kampel, M.: Evaluation of object tracking for aircraft activity surveillance. In: Proceedings of Joint IEEE International Workshop on VS-PETS, Beijing (2005)

  25. Thirde, D., Borg, M., Valentin, V., Barthélémy, L., Aguilera, J., Fernandez, G., Ferryman, J., cois Brémond, F., Thonnat, M., Kampel, M.: People and vehicle tracking for visual surveillance. In: 6th IEEE International Workshop on Visual Surveillance 2006. Graz, Austria (2006)

  26. Thirde, D., Borg, M., Valentin, V., Fusier, F., Aguilera, J., Ferryman, J., Brémond, F., Thonnat, M., Kampel, M.: Visual surveillance for aircraft activity monitoring. In: Proceedings of Joint IEEE International Workshop on VS-PETS, Beijing (2005)

  27. Tsai, R.: An efficient and accurate camera calibration technique for 3d machine vision. In: Proceedings of CVPR, pp. 323–344 (1986)

  28. Vu, V., Bremond, F., Thonnat, M.: Automatic video interpretation: a novel algorithm for temporal scenario recognition. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence, pp. 1295–1300 Acapulco, Mexico (2003)

  29. Wren C.R., Azarbayejani A., Darrell T. and Pentland A. (1997). Pfinder: real-time tracking of the human body. IEEE Trans. PAMI 19(7): 780–785

    Google Scholar 

  30. Xu G. and Zhang Z. (1996). Epipolar geometry in stereo, motion and object recognition: a unified approach. Kluwer, Dordrecht

    MATH  Google Scholar 

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Correspondence to François Brémond.

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Fusier, F., Valentin, V., Brémond, F. et al. Video understanding for complex activity recognition. Machine Vision and Applications 18, 167–188 (2007). https://doi.org/10.1007/s00138-006-0054-y

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