Similarity-based analysis for large networks of ultra-low resolution sensors

Pattern Recognition

Volumn 39, Issue 10, 2006, Pages 1918-1931

  Wren, Christopher R ^a   Minnen, David C ^a   Rao, Srinivasa G ^a  

^a MITSUBISHI ELECTRIC RESEARCH LABORATORIES   (United States)

Author keywords

Behavior; Context; Localization; Sensor networks; Similarity

Indexed keywords

GAIT ANALYSIS; GEOMETRY; LARGE SCALE SYSTEMS; MOTION PLANNING;

BEHAVIOR; CONTEXT; LOCALIZATION; SENSOR NETWORKS; SIMILARITY;

SENSOR DATA FUSION;

EID: 33745346218     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2006.04.009     Document Type: Article

References (23)

1. Wilson D.H., and Atkeson C. Simultaneous tracking and activity recognition (star) using many anonymous, binary sensors. The Third International Conference on Pervasive Computing (2005) 62-79
2. Stauffer C., and Grimson E. Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Recognition Mach. Intell. 22 8 (2000) 747-757
3. N. Johnson, D. Hogg, Learning the distribution of object trajectories for event recognition, Image Vision Comput. 14(8) (1996).
4. Minnen D., Essa I., and Starner T. Expectation grammars: leveraging high-level expectations for activity recognition. Workshop on Event Mining, Event Detection, and Recognition in Video, held in Conjunction with Computer Vision and Pattern Recognition vol. 2 (2003), IEEE 626
5. Cutler R., and Davis L. Real-time periodic motion detection, analysis and applications. Conference on Computer and Pattern Recognition. IEEE, Fort Collins, USA (1999) 326-331
6. Moeslund T.B., and Granum E. A survey of computer vision-based human motion capture. Comput. Vision Image Understanding 81 (2001) 231-268
7. Wilson A., and Bobick A. Realtime online adaptive gesture recognition. Proceedings of the International Conference on Pattern Recognition. Barcelona, Spain (2000) 111-116
8. Ivanov Y.A., and Blumberg B.M. Solving weak transduction with EM. Robotic Autonomous Syst. 39 3 (2002) 129-143
9. C.R. Wren, S.G. Rao, Self-configuring, lightweight sensor networks for ubiquitous computing, in: The Fifth International Conference on Ubiquitous Computing: Adjunct Proceedings, 2003, pp. 205-206, also MERL Technical Report TR2003-24.
10. Svaizer P., Matassoni M., and Omologo M. Acoustic source location in a three-dimensional space using cross-power spectrum phase. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing. Munich, Germany (1997) 231-234
11. Kruskal J.B., and Wish M. Multidimensional Scaling (1978), Sage Publications, Beverly Hills, CA
12. Duda R.O., Hart P.E., and Stork D.G. Pattern Classification. second ed. (2001), Wiley, New York
13. The Analytical Sciences Corporation, Applied Optimal Estimation, 1996.
14. Criminisi A., Reid I., and Zisserman A. A plane measuring device. Image Vision Comput. 17 8 (1999) 625-634
15. Cichocki A., and Amari S. Adaptive Blind Signal and Image Processing (2002), Wiley, New York
16. Smyth P. Clustering sequences with hidden markov models. In: Mozer M.C., Jordan M.I., and Petsche T. (Eds). Advances in Neural Information Processing Systems vol. 9 (1997), The MIT Press, Cambridge, MA 648. http://citeseer.nj.nec.com/smyth97clustering.html URL 〈citeseer.nj.nec.com/smyth97clustering.html〉
17. Wang T.-S., Shum H.-Y., Xu Y.-Q., and Zheng N.-N. Unsupervised analysis of human gestures. IEEE Pacific Rim Conference on Multimedia (2001) 174-181
18. Alon J., Sclaroff S., Kollios G., and Pavlovic V. Discovering clusters in motion time-series data. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2003) 375-381
19. D.C. Minnen, C.R. Wren, Finding temporal patterns by data decomposition, in: Sixth International Conference on Automatic Face and Gesture Recognition, IEEE, 2004, pp. 608-613, also MERL Technical Report TR2004-054.
20. Rabiner L.R. A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of IEEE 77 2 (1989) 257-285
21. Clarkson B.P., and Pentland A. Unsupervised clustering of ambulatory audio and video. Proceedings of the International Conference of Acoustics Speech and Signal Processing. Phoenix, Arizona (1999) 3037-3040
22. Toyama K., Krumm J., Brumitt B., and Meyers B. Wallflower: principles and practice of background maintenance. ICCV (1999), IEEE 255-261
23. C.R. Wren, D.C. Minnen, Activity mining in sensor networks, in: Workshop on Activity Recognition and Discovery, NIPS, 2004, also MERL Technical Report TR2004-135.