Abstract
This chapter describes an approach for people localization and tracking in an office environment using a sensor network that consists of video cameras, infrared tag readers, a fingerprint reader, and a PTZ camera. The approach is based on a Bayesian framework that uses noisy, but redundant data from multiple sensor streams and incorporates it with the contextual and domain knowledge that is provided by both the physical constraints imposed by the local environment where the sensors are located and by the people who are involved in the surveillance tasks. The experimental results are presented and discussed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Fuentes, LM, Velastin, SA. People tracking in surveillance applications. In: Proc. 2nd IEEE International Workshop on PETS, Kauai, Hawaii, USA, December 2001.
Siebel, NT, Maybank, S. Fusion of Multiple Tracking Algorithms for Robust People Tracking. Proc. 7th European Conference on Computer Vision (ECCV 2002), Copenhagen, Denmark, May 2002; IV:373–387.
Krumm, J, Harris, S Meyers, B Brumitt, B Hale, M Shafer, S. Multi-camera multi-person tracking for easy living. In: Proc. 3rd IEEE InternationalWorkshop on Visual Surveillance, July 1, 2000, Dublin, Ireland.
Mittal, A, Davis, LS.M2Tracker:Amulti-viewapproach to segmenting and tracking people in a cluttered scene. International Journal of Computer Vision, 2003; 51(3):189–203.
Kettnaker, V, Zabih, R. Bayesian multi-camera surveillance. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, Fort Collins, Colorado, June 23–25,1999;2253–2259.
Cai, Q, Aggarwal, JK. Tracking human Motion using multiple cameras. In: Proc. International Conference on Pattern Recognition, Vienna, Austria, August 1996:68–72.
Cai, Q, Aggarwal, JK. Tracking human motion in structured environments using a distributed-camera system. IEEE Transactions on Pattern Analysis and Machine Intelligence, (1999) 2(11):1241–1247.
Khan S, Javed O, Rasheed Z, Shah M. Human tracking in multiple cameras. In: Proc. 8th IEEE International Conference on Computer Vision, Vancouver, Canada, July 9–12, 2001;1:331–336.
Javed O, Rasheed Z, Atalas O, Shah, M. KnightM: A real time surveillance system for multiple overlapping and non-overlapping cameras. In: The fourth IEEE International Conference on Multimedia and Expo (ICME 2003), Baltimore, MD, July 6–9, 2003.
Huang KS, Trivedi MM. Distributed video arrays for tracking, human identification, and activity analysis. In: The fourth IEEE International Conference on Multimedia and Expo (ICME 2003), Baltimore, MD, July 6–9, 2003;2:9–12.
Cheung S-CS, Kamath Ch. Robust techniques for background subtraction in urban traffic video. In: Proc. of SPIE, Visual Communications and Image Processing 2004, S. Panchanathan, B. Vasudev (Eds), January 2004;5308:881–892
Toyama K, Krumm J, Brumitt B, Mayers B. Wallflower: Principles and practice of background maintenance. In: Intl Conference on Computer Vision (ICCV), 1999;255–261.
Stauffer C, Grimson WEL. Adaptive background mixture models for real-time tracking. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1999;246–252.
Power PW, Schoonees JA. Understanding background mixture modeling for foreground segmentation. In: Proc. Image and Vision Computing, New Zealand, 2002;267–271.
Nefian AV, Hayes III. M.H. Maximum likelihood training of the embedded HMM for face detection and recognition. IEEE International Conference on Image Processing, September 2000;1:33–36.
Grewal MS, Andrews AP. Kalman Filtering. Theory and Practice Using Matlab. John Wiley & Sons, 2001.
Ristic B, Arulampalam S, Gordon N. Beyond the KalmanFilter.ParticleFilters for Tracking Applications. Artech House: Boston, London, 2004.
Stone LD, Barlow CA, Corwin TL. Bayesian Multiple Target Tracking. Artech House: Boston, London, 1999.
Hall DL, McMullen SAH. Mathematical Techniques in Multisensor Data Fusion. Artech House: Boston, London, 2004.
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer
About this chapter
Cite this chapter
Wei, G., Petrushin, V.A., Gershman, A.V. (2007). Multiple-Sensor People Localization in an Office Environment. In: Petrushin, V.A., Khan, L. (eds) Multimedia Data Mining and Knowledge Discovery. Springer, London. https://doi.org/10.1007/978-1-84628-799-2_21
Download citation
DOI: https://doi.org/10.1007/978-1-84628-799-2_21
Publisher Name: Springer, London
Print ISBN: 978-1-84628-436-6
Online ISBN: 978-1-84628-799-2
eBook Packages: Computer ScienceComputer Science (R0)