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Multiple sensor integration for indoor surveillance

Published: 21 August 2005 Publication History

Abstract

Multiple Sensor Indoor Surveillance (MSIS) is a research project at Accenture Technology Labs aimed at exploring a variety of redundant sensors in a networked environment where each sensor is giving noisy information and the goal is to coherently reason about some aspect of the environment. We describe the objectives of the project, the problems it was designed to solve and some recent results. The environment includes 32 web cameras, an infrared badge ID system, a PTZ camera, and a fingerprint reader. We discuss two concrete problems that we have tackled in this project: (1) Visualizing events detected by 32 cameras during 24 hours, and (2) Localizing people using fusion of multiple streams of noisy sensory data with the contextual and domain knowledge that is provided by both the physical constraints imposed by the local environment and by the people that are involved in the surveillance tasks. We use Self-Organizing Maps to approach the first problem and suggest a Bayesian framework for the second one. The experimental data are provided and discussed.

References

[1]
Kanade, T., Collins, R. T., and Lipton, A. J. "Advances in Cooperative Multi-Sensor Video Surveillance", Proc. DARPA Image Understanding Workshop, Morgan Kaufmann, pp. 3--24, Nov. 1998.
[2]
Siebel, N. T. and Maybank, S., "Fusion of Multiple Tracking Algorithms for Robust People Tracking". Proc. 7th European Conference on Computer Vision (ECCV 2002), Copenhagen, Denmark, vol. IV, pp. 373--387, May 2002.
[3]
Krumm, J., Harris, S., Meyers, B., Brumitt, B., Hale, M. Shafer, S., "Multi-camera Multi-person Tracking for EasyLiving". Proc. 3rd IEEE International Workshop on Visual Surveillance, Dublin, Ireland, July 1, 2000.
[4]
J.-H. Oh, J.-K. Lee, S. Kote, B. Bandi, "Multimedia Data Mining Framework for Raw Video Sequences". In O. R. Zaiane, S. J. Simoff, and Ch. Djeraba (Eds.) Mining Multimedia and Complex Data. Lecture Notes in Artificial Intelligence. Vol. 2797. Springer, pp. 18--35, 2003.
[5]
H. Zhong and J. Shi Finding, "(Un)Usual Events in Video", Tech. Report CMU-RI-TR-03-05, 2003.
[6]
T. Kohonen, Self-Organizing Maps, Springer-Verlag, 1997.
[7]
J. Vesanto, J. Himberg, E. Alhoniemi, J. Parhankangas, "SOM Toolbox for Matlab 5", Helsinki University of Technology, Report A57, 2000.
[8]
J. Vesanto and I. Alhoniemi, "Clustering of Self-organizing Maps", IEEE Trans. on Neural Network, 11, 3, pp.586--600, 2000.
[9]
D. L. Davies and D. W. Bouldin "A cluster separation measure", IEEE Trans. on Pattern Analysis and Machine Intelligence, PAMI-1, pp. 224--227, 1979.
[10]
A. V. Nefian and M. H. Hayes III. "Maximum likelihood training of the embedded HMM for face detection and recognition", IEEE International Conference on Image Processing, vol. 1, pp. 33--36, September 2000.
[11]
G. Wei, V. A. Petrushin, and A. V. Gershman "Multiple-Camera People Localization in a Cluttered Environment", In Proc. Multimedia Data Mining Workshop 2004

Cited By

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  • (2009)An access control and time management software solution using RFIDProceedings of the International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing10.1145/1731740.1731795(1-6)Online publication date: 18-Jun-2009
  • (2009)A flexible framework for multisensor data fusion using data stream management technologiesProceedings of the 2009 EDBT/ICDT Workshops10.1145/1698790.1698821(193-200)Online publication date: 22-Mar-2009

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cover image ACM Other conferences
MDM '05: Proceedings of the 6th international workshop on Multimedia data mining: mining integrated media and complex data
August 2005
107 pages
ISBN:159593216X
DOI:10.1145/1133890
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 August 2005

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Author Tags

  1. Bayesian inference
  2. indoor surveillance
  3. multi-camera surveillance
  4. people localization
  5. rare event detection
  6. self-organizing maps
  7. unsupervised learning
  8. visualization

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Cited By

View all
  • (2009)An access control and time management software solution using RFIDProceedings of the International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing10.1145/1731740.1731795(1-6)Online publication date: 18-Jun-2009
  • (2009)A flexible framework for multisensor data fusion using data stream management technologiesProceedings of the 2009 EDBT/ICDT Workshops10.1145/1698790.1698821(193-200)Online publication date: 22-Mar-2009

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