|
ABSTRACT
Most surveillance and monitoring systems nowadays utilize multiple types of sensors. However, due to the asynchrony among and diversity of sensors, information assimilation - how to combine the information obtained from asynchronous and multifarious sources is an important and challenging research problem. In this paper, we propose a hierarchical probabilistic method for information assimilation in order to detect events of interest in a surveillance and monitoring environment. The proposed method adopts a bottom-up approach and performs assimilation of information at three different levels - media-stream level, atomic-event level and compound-event level.To detect an event, our method uses not only the current media streams but it also utilizes their two important properties - first, accumulated past history of whether they have been providing the concurring or contradictory evidences, and - second, the system designer's confidence in them. A compound event, which comprises of two or more atomic-events, is detected by first estimating probabilistic decisions for the atomic-events based on individual streams, and then by aligning these decisions along a timeline and hierarchically assimilating them. The experimental results show the utility of our method.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
| |
1
|
CNN news, July 2005. http://www.cnn.com/2005/LAW/07/05/crime.prevention.ap/index.html.
|
 |
2
|
|
| |
3
|
P. K. Atrey and M. S. Kankanhalli. Goal based optimal selection of media streams. In IEEE International Conference on Multimedia and Expo, Amsterdam, The Netherlands, July 2005.
|
| |
4
|
J. A. Benediktsson and I. Kanellopoulos. Classification of multisource and hyperspectral data based on decision fusion. IEEE Trans. on GeoScience and Remote Sensing, 37(3):1367--1377, May 1999.
|
| |
5
|
D. A. Bloch and H. C. Kraemer. 2 x 2 Kappa coefficients: Measures of agreement or association. Journal of Biometrics, 45(1):269--287, 1989.
|
| |
6
|
Z. Chair and P. Varshney. Optimal data fusion in multiple sensor detection systems. IEEE Transactions on Aerospace and Electronic Systems, 22:98--101, 1986.
|
| |
7
|
N. Checka, K. W. Wilson, M. R. Siracusa, and T. Darrell. Multiple person and speaker activity tracking with a particle filter. In International Conference on Acoustics Speech and Signal Processing, Montreal, Canada, May 2004.
|
| |
8
|
C. Genest and J. V. Zidek. Combining probability distributions: A critique and annotated bibliography. Journal of Statistical Science, 1(1):114--118, 1986.
|
| |
9
|
J. Hershey, H. Attias, N. Jojic, and T. Krisjianson. Audio visual graphical models for speech processing. In IEEE International Conference on Speech, Acoustics, and Signal Processing, Montreal, Canada, May 2004.
|
| |
10
|
P. KaewTraKulPong and R. Bowden. An improved adaptive background mixture model for real-time tracking with shadow detection. In European Workshop on Advanced Video Based Surveillance Systems, London, UK, September 2001.
|
| |
11
|
M. Kam, Q. Zhu, and W. Gray. Optimal data fusion of correlated local decisions in multiple sensor detection systems. IEEE Transactions on Aerospace and Electronic Systems, 28(3):916--920, July 1992.
|
| |
12
|
L. I.-K. Lin. A concordance correlation coefficient to evaluate reproducibility. Journal of Biometrics, 45(1):255--268, 1989.
|
| |
13
|
A. V. Nefian, L. Liang, X. Pi, X. Liu, and K. Murphy. Dynamic bayesian networks for audio-visual speech recognition. In EURASIP Journal on Applied Signal Processing, volume 11, pages 1274--1288, 2002.
|
 |
14
|
|
| |
15
|
B. S. Rao and H. D. Whyte. A decentralized bayesian algorithm for identification of tracked objects. IEEE Transactions on Systems, Man and Cybernetics, 23(6):1683--1698, November-December 1993.
|
| |
16
|
M. Siegel and H. Wu. Confidence fusion. In IEEE International Workshop on Robot Sensing, pages 96--99, Graz, Austria, May 2004.
|
| |
17
|
C. Stauffer and W. E. L. Grimson. Adaptive background mixture models for real-time tracking. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 2, pages 252--258, Ft. Collins, CO, USA, 1999.
|
| |
18
|
M. Valera and S. A. Velastin. Intelligent distributed surveillance systems: A review. IEE Proceedings on Visual Image Signal Processing, 152(2):192--204, April 2005.
|
 |
19
|
Yi Wu , Edward Y. Chang , Kevin Chen-Chuan Chang , John R. Smith, Optimal multimodal fusion for multimedia data analysis, Proceedings of the 12th annual ACM international conference on Multimedia, October 10-16, 2004, New York, NY, USA
[doi> 10.1145/1027527.1027665]
|
CITED BY 6
|
|
Andreas Girgensohn , Frank Shipman , Anthony Dunnigan , Thea Turner , Lynn Wilcox, Support for effective use of multiple video streams in security, Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks, October 27-27, 2006, Santa Barbara, California, USA
|
|
|
|
|
|
Andreas Girgensohn , Frank Shipman , Thea Turner , Lynn Wilcox, Effects of presenting geographic context on tracking activity between cameras, Proceedings of the SIGCHI conference on Human factors in computing systems, April 28-May 03, 2007, San Jose, California, USA
|
|
|
Andreas Girgensohn , Don Kimber , Jim Vaughan , Tao Yang , Frank Shipman , Thea Turner , Eleanor Rieffel , Lynn Wilcox , Francine Chen , Tony Dunnigan, DOTS: support for effective video surveillance, Proceedings of the 15th international conference on Multimedia, September 25-29, 2007, Augsburg, Germany
|
|
|
Vivek K. Singh , Hamed Pirsiavash , Ish Rishabh , Ramesh Jain, Towards environment-to-environment (E2E) multimedia communication systems, Proceeding of the 1st ACM international workshop on Semantic ambient media experiences, October 31-31, 2008, Vancouver, British Columbia, Canada
|
|
|
|
|