Journal of Multimedia, Vol 6, No 1 (2011), 3-13, Feb 2011
doi:10.4304/jmm.6.1.3-13

A Framework For An Event Driven Video Surveillance System

Declan Kieran, Jonathan Weir, WeiQi Yan

Abstract


In this paper we present an event driven surveillance system that uses multiple cameras. The purpose of this system is to enable thorough exploration of surveillance events. The system uses a client-server web architecture as this provides scalability for further development of the system infrastructure. The system is designed to be accessed by surveillance operators who can review and comment on events generated by our event detection processing modules. We do not just focus on event detection, but are working towards the optimization of event detection. A multiple camera network system that tracks a moving object (or person) and decides if this is an event of interest is also examined. Dynamic switching of the cameras is implemented to aid in human monitoring of the network. The camera displayed in the main view should be the camera with the most interesting activity occurring. Unusual activity is defined as activity occurring that is not of the norm. Normal activity is considered to be everyday repeated activity. Further thought will be given to the extension of this system into a distributed system that would effectively create an event web system. Our contributions are to the development of automated real-time switching of camera views to aid camera operators in the effort of effective video surveillance, and also the detection of events of interest within a surveillance environment, with appropriate alerts and storage of these events. To the best of our knowledge this system provides a novel approach to the technological surveillance paradigm.


Keywords


surveillance; event driven surveillance; video surveillance; video surveillance systems;

References


[1] W. Yan, D. Kieran, S. Rafatirad, and R. Jain, “A comprehensive study of visual event computing,” Multimedia Tools and Applications, pp. 1–39, 2010.

[2] P. Atrey, A. El Saddik, and M. Kankanhalli, “Effective multimedia surveillance using a human-centric approach,” Multimedia Tools and Applications, pp. 1–25, 2010.

[3] M. Valera and S. A. Velastin, “Intelligent distributed surveillance systems: a review,” IEE Proceedings - Vision, Image and Signal Processing, vol. 152, no. 2, pp. 192–204, 2005.
doi:10.1049/ip-vis:20041147

[4] L. Zelnik-Manor and M. Irani, “Event-based analysis of video,” IEE Proceedings - Computer Society Conference on CVPR, vol. 2, no. 2, pp. 123–130, 2001.

[5] J. Baulier, S. Blott, H. F. Korth, and A. Silberscharz, “A database system for real-time event aggregation in telecommunication,” in Proc. 24th Int. Conf. Very Large Data Bases, VLDB, 24–27 1998, pp. 680–684.

[6] G. S. Pingali, Y. Jean, A. Opalach, and I. Carlbom, “Lucentvision: Converting real world events into multimedia experiences,” Proceedings of the IEEE International Conference on Multimedia and Expo, vol. 3, pp. 1433–1436, 2000.

[7] S. Hongeng and R. Nevatia, “Large-scale event detection using semi-hidden markov models,” in Proceedings of the Ninth IEEE International Conference on Computer Vision, 2003, pp. 1455– 1462.
doi:10.1109/ICCV.2003.1238661

[8] J. Xu, C.and Wang, K. Wan, Y. Li, and L. Duan, “Live sports event detection based on broadcast video and web-casting text,” in MULTIMEDIA ’06: Proceedings of the 14th annual ACM international conference on Multimedia, 2006, pp. 221–230.

[9] S. Park and J. K. Aggarwal, “Event semantics in two-person interactions,” International Conference on Pattern Recognition, vol. 4, pp. 227–230, 2004.

[10] A. R. J. Francois, R. Nevatia, J. Hobbs, and R. C. Bolles, “Verl: An ontology framework for representing and annotating video events,” IEEE MultiMedia, vol. 12, no. 4, pp. 76–86, 2005.
doi:10.1109/MMUL.2005.87

[11] H. Zhou and D. Kimber, “Unusual event detection via multicamera video mining,” in ICPR ’06: Proceedings of the 18th International Conference on Pattern Recognition, 2006, pp. 1161–1166.

[12] D. Zhang, D. Gatica-Perez, S. Bengio, and I. McCowan, “Semisupervised adapted hmms for unusual event detection,” in CVPR ’05: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, 2005, pp. 611–618.

[13] E. L. Andrade, S. Blunsden, and R. B. Fisher, “Modelling crowd scenes for event detection,” in ICPR ’06: Proceedings of the 18th International Conference on Pattern Recognition, 2006, pp. 175–178.

[14] P. Atrey, M. Kankanhalli, and R. Jain, “Information assimilation framework for event detection in multimedia surveillance systems,” Multimedia Systems, vol. 12, pp. 239–253, 2006.
doi:10.1007/s00530-006-0063-8

[15] G. L. Foresti and C. Mahoen, P. Regazzoni, Multimedia Video-Based Surveillance System, Requirements, Issues and Solutions. Dordrecht, The Netherlands: Kluwer Academic Publishers, 2002.

[16] C. Regazzoni, G. Fabri, and G. Vernazzza, Advanced Videobased Surveillance System. Dordrecht, The Netherlands: Kluwer Academic Publishers, 2002.

[17] P. Remagnio, G. Jones, N. Paragios, and C. Regazzoni, Videobased Surveillance Systems, Computer vision and Distributed processing. Dordrecht, The Netherlands: Kluwer Academic Publishers, 2002.

[18] G. Wu, Y. Wu, L. Jiao, Y. F. Wang, and E. Chang, “Multicamera spatio-temporal fusion and biased sequence data learning for security surveillance,” Pmc. of ACM Multimedia, pp. 528–538, 2003.

[19] Y. Wu, L. Jiao, G. Wu, E. Chang, and Y. F. Wang, “Feature extraction and biased statistical inference for video surveillance,” Proc. of IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 284–289, 2003.

[20] P. H. Kelly, A. Katkere, D. Y. Kumura, S. Moezzi, S. Chatterjee, and R. Jain, “An architecture for multiple perspective interactive video,” Proc. of ACM Multimedia, pp. 201–212, 1995.

[21] S. Santini and R. Jain, “A multiple perspective interactive video architecture for vsam,” Proc. of the 1998 Image Understanding Workshop, pp. 51–55, 1998.

[22] R. Jain, “Experiential computing,” Communications of the ACM, vol. 46, no. 7, pp. 48–55, 2003.
doi:10.1145/792704.792729

[23] J. Davis and X. Chen, “Calibrating pan-tilt cameras in wide-area surveillance networks,” Proc. of IEEE International Conference on Computer Vision, vol. 1, no. 1, pp. 144–149, 2003.
doi:10.1109/ICCV.2003.1238329

[24] W. Yan, S. Kankanhalli, and M. J. T. Wang, J. Reinders, “Experiential sampling for monitoring.” Proc. of First ACM International Workshop on Experiential Telepresentation, pp. 77–86, 2003.

[25] W. Jun, M. S. Kankanhalli, W. Yan, and R. Jain, “Experiential sampling for video surveillance,” Proc. of First ACM International Workshop on Video Surveillance, pp. 77–86, 2003.

[26] G. L. Foresti, C. Micheloni, L. Snidaro, P. Remagnino, and T. Ellis, “Active video-based surveillance system,” IEEE Signal Processing Magazine, vol. 22, no. 2, pp. 25–37, 2005.
doi:10.1109/MSP.2005.1406473

[27] J. R. Parker, “Gray-level thresholding in badly illuminated images,” IEEE Trans. Pattern Anal. Machine Intell, vol. 13, no. 8, pp. 813–819, 1991.
doi:10.1109/34.85672

[28] G. L. Foresti, “Object detection and tracking in time-varying and badly illuminated outdoor environments,” IEEE Trans. Pattern Anal. Machine Intell, vol. 37, no. 9, pp. 2550–2564, 1999.

[29] D. Vinay, D. Harwood, and L. S. Davis, “Multivalued default logic for identity maintenance in visual surveillance,” 9th European Conference on Computer Vision, no. 9, pp. 119–132, 2006.

[30] S. Hongeng, F. Bremond, and R. Nevatia, “Representation and optimal recognition of human activities,” IEEE Proc. Comput. Vision Pattern Recogn, vol. 1, no. 1, pp. 818–825, 2000.

[31] L. R. Rabiner, “A tutorial on hidden markov models and selected applications in speech recognition,” IEEE Proc. IEEPAD, vol. 77, no. 2, pp. 257–286, 1989.

[32] D. Zhang, D. Gatica-Perez, S. Bengio, and I. McCowan, “Semisupervised adapted hmms for unusual event detection,” Proc. of IEEE CVPR, vol. 1, no. 1, pp. 611–618, 2005.

[33] M. Al-Hames and G. Rigoll, “A multi-modal mixed-state dynamic bayesian network for robust meeting event recognition from disturbed data,” IEEE Proc ICME, pp. 45–48, 2005.

[34] E. Stringa, C. Sacchi, and C. S. Regazzoni, “A multimedia system for surveillance of unattended railway stations,” Proc. of Eusipco, pp. 1709–1712, 1998.

[35] K. Alahari and C. V. Jawahar, “Discriminative actions for recognising events,” ICVGIP, pp. 552–563, 2006.

[36] D. B. Rubin, Using the SIR Algorithm to Simulate Posterior Distributions (with discussion), in Bayesian Statistics, 3rd ed. New York: Oxford University Press, 1998.

[37] N. J. Gordon, D. J. Salmond, and A. F. M. Smith, “Novel approach to nonlinear/non-gaussian bayesian state estimation,” IEEE Proceedings, vol. 140, no. 2, pp. 107–113, 1993.

[38] A. Doucet, S. J. Godsill, and C. Andrieu, “On sequential monte carlo sampling methods for bayesian filtering,” Statist. Comp, vol. 10, no. 3, pp. 197–208, 2000.
doi:10.1023/A:1008935410038

[39] M. Isard and A. Blake, “Condensation-conditional density propagation for visual tracking,” International Journal on Computer Vision, vol. 29, no. 1, pp. 5–28, 1998.
doi:10.1023/A:1008078328650

[40] R. Jain, “Eventweb: Developing a human-centered computing system,” Computer, vol. 41, pp. 42–50, 2008.
doi:10.1109/MC.2008.49

[41] W. Yan and R. Jain, “Event detection from picture observations,” Proc. of International Workshop for Image Technology (IWAIT’08), 2008.


Full Text: PDF


Journal of Multimedia (JMM, ISSN 1796-2048)

Copyright @ 2006-2012 by ACADEMY PUBLISHER – All rights reserved.