skip to main content
research-article

Modeling and assessing quality of information in multisensor multimedia monitoring systems

Published:04 February 2011Publication History
Skip Abstract Section

Abstract

Current sensor-based monitoring systems use multiple sensors in order to identify high-level information based on the events that take place in the monitored environment. This information is obtained through low-level processing of sensory media streams, which are usually noisy and imprecise, leading to many undesired consequences such as false alarms, service interruptions, and often violation of privacy. Therefore, we need a mechanism to compute the quality of sensor-driven information that would help a user or a system in making an informed decision and improve the automated monitoring process. In this article, we propose a model to characterize such quality of information in a multisensor multimedia monitoring system in terms of certainty, accuracy/confidence and timeliness. Our model adopts a multimodal fusion approach to obtain the target information and dynamically compute these attributes based on the observations of the participating sensors. We consider the environment context, the agreement/disagreement among the sensors, and their prior confidence in the fusion process in determining the information of interest. The proposed method is demonstrated by developing and deploying a real-time monitoring system in a simulated smart environment. The effectiveness and suitability of the method has been demonstrated by dynamically assessing the value of the three quality attributes with respect to the detection and identification of human presence in the environment.

References

  1. Atrey, P. K. and El Saddik, A. 2008. Confidence evolution in multimedia systems. IEEE Trans. Multimed. 10, 7, 1288--1298. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Atrey, P. K., Kankanhalli, M. S., and Jain, R. 2006. Information assimilation framework for event detection in multimedia surveillance systems. Multimed. Syst. 12, 3, 239--253.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Baeza-Yates, R. and Ribeiro-Neto, B. 1999. Modern Information Retrieval. Addison Wesley, New York, ACM Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Ballou, D. P. and Tayi, G. K. 1999. Enhancing data quality in data warehouse environments. Comm. ACM 42, 1, 73--78. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Beccari, G., Caselli, S., and Zanichelli, F. 2005. A technique for adaptive scheduling of soft real-time tasks. Real-Time Syst. 30, 3, 187--215. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Bisdikian, C. 2007. On sensor sampling and quality of information: A starting point. In Proceedings of the Workshop on Pervasive Communications. 279--284. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Blasch, E. and Plano, S. 2005. DFIG level 5 (user refinement) issues supporting situational assessment reasoning. In Proceedings of the 8th International Conference on Information Fusion. Vol. 1. xxxv--xliii.Google ScholarGoogle Scholar
  8. Carvalho, H., Heinzelman, W., Murphy, A., and Coelho, C. 2003. A general data fusion architecture. In Proceedings of the 6th International Conference on Information Fusion. Vol. 2. 1465--1472.Google ScholarGoogle Scholar
  9. Chen, D., Yang, J., Malkin, R., and Wactlar, H. D. 2007. Detecting social interactions of the elderly in a nursing home environment. ACM Trans. Multimed. Comput. Comm. Appl. 3, 1, 6. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Ehikioya, S. 1999. A characterization of information quality using fuzzy logic. In Proceedings of the 18th International Conference of the North American Fuzzy Information Processing Society (NAFIPS). 635--639.Google ScholarGoogle ScholarCross RefCross Ref
  11. Hall, D. L. and Llinas, J. 1997. An introduction to multisensor fusion. In Proc. IEEE. 85, 1, 6--23.Google ScholarGoogle ScholarCross RefCross Ref
  12. Han, Q. and Venkatasubramanian, N. 2007. Timeliness-accuracy balanced collection of dynamic context data. IEEE Trans. Para. Distrib. Syst. 18, 2, 158--171. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Hossain, M. A., Atrey, P. K., and El Saddik, A. 2007a. Modeling quality of information in multi-sensor surveillance systems. In Proceedings of the IEEE ICDE Workshop on Ambient Intelligence, Media, and Sensing (AIMS). 11--18. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Hossain, M. A., Atrey, P. K., and El Saddik, A. 2007b. Smart mirror for ambient home environment. In Proceedings of the 3rd IET International Conference on Intelligent Environments (IE'07). 589--596.Google ScholarGoogle ScholarCross RefCross Ref
  15. Hossain, M. A., Atrey, P. K., and El Saddik, A. 2008. Context-aware QoI computation in multi-sensor systems. In Proceedings of the 1st IEEE Workshop on Quality of Information (QoI) for Sensor Networks (QoISN'08).Google ScholarGoogle ScholarCross RefCross Ref
  16. Hughes, K. and Ranganathan, N. 1993. A model for determining sensor confidence. In Proceedings of the IEEE International Conference on Robotics and Automation. Vol. 2. 136--141.Google ScholarGoogle Scholar
  17. Jain, A. K., Murty, M. N., and Flynn, P. J. 1999. Data clustering: a review. ACM Comput. Surv. 31, 3, 264--323. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Kahn, B., Strong, D., and Wang, R. 2002. Information quality benchmarks: Product and service performance. Comm. ACM 45, 4, 184--192. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Klein, A., Do, H.-H., Karnstedt, M., and Lehner, W. 2007. Representing data quality for streaming and static data. In Proceedings of the IEEE ICDE Workshop on Ambient Intelligence, Media, and Sensing (AIMS). 3--10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Knuth, D. E. 1981. The Art of Computer Programming. Vol. 2: Seminumerical Algorithms. Atmospheric Chemistry & Physics.Google ScholarGoogle Scholar
  21. Lazarevic-McManus, N., Renno, J., and Jones, G. A. 2006. Performance evaluation in visual surveillance using the F-measure. In Proceedings of the 4th ACM International Workshop on Video Surveillance and Sensor Networks (VSSN). 45--52. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Mariano, V., Min, J., Park, J.-H., Kasturi, R., Mihalcik, D., Li, H., Doermann, D., and Drayer, T. 2002. Performance evaluation of object detection algorithms. In Proceedings of the 16th International Conference on Pattern Recognition (ICPR). Vol. 3. 965--969. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Miller, H. 1996. The multiple dimensions of information quality. Inform. Syst. Manage. 13, 2, 79--82.Google ScholarGoogle ScholarCross RefCross Ref
  24. Muller-Schneiders, S., Jager, T., Loos, H., and Niem, W. 2005. Performance evaluation of a real time video surveillance systems. In Proceedings of the Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS). 137--143. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Nakamura, E. F., Loureiro, A. A. F., and Frery, A. C. 2007. Information fusion for wireless sensor networks: Methods, models, and classifications. ACM Comput. Surv. 39, 3, 9. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Nascimento, J. and Marques, J. 2006. Performance evaluation of object detection algorithms for video surveillance. IEEE Trans. Multimed. 8, 4, 761--774. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Neus, A. 2001. Managing information quality in virtual communities of practice. In Proceedings of the 6th International Conference on Information Quality. E. Pierce and R. Katz-Haas, Eds. Sloan School of Management., Boston, MA.Google ScholarGoogle Scholar
  28. Peng, L. and Candan, K. S. 2005. Confidence-driven early object elimination in quality-aware sensor workflows. In Proceedings of the 2nd International Workshop on Data Management for Sensor Networks (DMSN'05). ACM, New York, NY, 45--51. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Peng, L. and Candan, K. S. 2007. Predictive early object shedding in media processing workflows. In Proceedings of the IEEE International Conference on Multimedia and Expo (ICME'07). 1191--1194.Google ScholarGoogle Scholar
  30. Radke, R., Andra, S., Al-Kofahi, O., and Roysam, B. 2005. Image change detection algorithms: a systematic survey. IEEE Trans. Image Process. 14, 3, 294--307. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Sastry, S. and Iyengar, S. S. 2005. Real-time sensor-actuator networks. Int. J. Distrib. Sensor Netw. 1, 1, 17--34.Google ScholarGoogle ScholarCross RefCross Ref
  32. Schlogl, T., Beleznai, C., Winter, M., and Bischof, H. 2004. Performance evaluation metrics for motion detection and tracking. In Proceedings of the 17th International Conference on Pattern Recognition (ICPR). Vol. 4. 519--522. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Siegel, M. and Wu, H. 2004. Confidence fusion. In Proceedings of the IEEE International Workshop on Robot Sensing. 96--99.Google ScholarGoogle Scholar
  34. Snidaro, L., Niu, R., Foresti, G. L., and Varshney, P. K. 2007. Quality-based fusion of multiple video sensors for video surveillance. IEEE Trans. Systems, Man, Cybernet. Part B 37, 4, 1044--1051. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Stauffer, C. and Grimson, W. E. L. 1999. Adaptive background mixture models for real-time tracking. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 252--258.Google ScholarGoogle Scholar
  36. Wald, L. 1999. Some terms of reference in data fusion. IEEE Trans. Geosci. Remote Sens. 37, 3, 1190--1193.Google ScholarGoogle Scholar
  37. Wang, J., Kankanhalli, M., Yan, W.-Q., and Jain, R. 2003. Performance evaluation of a real time video surveillance systems. In Proceedings of the ACM Workshop on Video Surveillance (IWVS). Berkeley, CA, USA.Google ScholarGoogle Scholar
  38. Wang, R. and Strong, D. 1996. Beyond accuracy: what data quality means to data consumers. J. Manage. Inform. Syst. 12, 4, 5--34. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Welford, B. P. 1962. Note on a method for calculating corrected sums of squares and products. Technometrics 4, 3 (Aug.), 419--420.Google ScholarGoogle ScholarCross RefCross Ref
  40. Yates, D. J., Nahum, E. M., Kurose, J. F., and Shenoy, P. 2008. Data quality and query cost in pervasive sensing systems. Perva. Mobile Comput. 4, 6, 851--870. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Ziliani, F., Velastin, S., Porikli, F., Marcenaro, L., Kelliher, T., Cavallaro, A., and Bruneaut, P. 2005. Performance evaluation of event detection solutions: the creds experience. In Proceedings of the IEEE Conference on Advanced Video and Signal-Based Surveillance. 201--206.Google ScholarGoogle Scholar

Index Terms

  1. Modeling and assessing quality of information in multisensor multimedia monitoring systems

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in

          Full Access

          • Published in

            cover image ACM Transactions on Multimedia Computing, Communications, and Applications
            ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 7, Issue 1
            January 2011
            158 pages
            ISSN:1551-6857
            EISSN:1551-6865
            DOI:10.1145/1870121
            Issue’s Table of Contents

            Copyright © 2011 ACM

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 4 February 2011
            • Accepted: 1 August 2009
            • Revised: 1 February 2009
            • Received: 1 August 2008
            Published in tomm Volume 7, Issue 1

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • research-article
            • Research
            • Refereed

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader
          About Cookies On This Site

          We use cookies to ensure that we give you the best experience on our website.

          Learn more

          Got it!