Skip to main content
SpringerLink
Log in

Behavior Modeling for Detection, Identification, Prediction, and Reaction (DIPR) in AI Systems Solutions

  • Chapter
Handbook of Ambient Intelligence and Smart Environments

  • 3022 Accesses

Abstract

The application need for distributed artificial intelligence (AI) systems for behavior analysis and prediction is a requirement today versus a luxury of the past. The advent of distributed AI systems with large numbers of sensors and sensor types and unobtainable network bandwidth is also a key driving force. Additionally, the requirement to fuse a large number of sensor types and inputs is required and can now be implemented and automated in the AI hierarchy, and therefore, this will not require human power to observer, fuse, and interpret.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Goshorn, R.: Sequential Behavior Classification Using Augmented Grammars. M.S. thesis, UCSD (2001)

    Google Scholar 

  2. Goshorn, R.: Syntactical Classification of Extracted Sequential Spectral Features Adapted to Priming Selected Interference Cancelers. Ph.D. thesis, UCSD (2005)

    Google Scholar 

  3. Sipser, M.: Theory of Computation. PWS Publishing Co., MA (1997)

    MATH  Google Scholar 

  4. Goshorn, R., Goshorn, J., Goshorn, D., Aghajan, H.: Architecture for Cluster-based Automated Surveillance Network for Detecting and Tracking Multiple Persons. International Conference on Distributed Smart Cameras, ICDSC (2007)

    Google Scholar 

  5. Goshorn, R., Goshorn, D., Goshorn, J., Goshorn, L.: Abnormal Behavior-Detection Using Sequential Syntactical Classification in a Network of Clustered Cameras. International Conference on Distributed Smart Cameras, ICDSC (2008)

    Google Scholar 

  6. Goshorn, D.: The Enhancement of Low-Level Classifications in Sequential Syntactic High-Level Classifiers Compuer Science. PhD Research Exam, UCSD (2008)

    Google Scholar 

  7. Goshorn, R., Goshorn, D.: Vision-Based Syntactical Classification of Hand Gestures to Enable Robust Human Computer Interaction. Workshop on AI Techniques for Ambient Intelligence, co-located with European Conference on Ambient Intelligence, ECAI (2008)

    Google Scholar 

  8. Goshorn, R., Goshorn, D., Kölsch, M.: The Enhancement of Low-level Classifications for Ambient Assisted Living. Behavioral Monitoring and Interpretation Workshop at the German Conference on Aritificial Intelligence, KI (2008)

    Google Scholar 

  9. Kölsch, M.: Vision Based Hand Gesture Interfaces for Wearable Computing and Virtual Environments. Ph.D. thesis, UCSB (2004)

    Google Scholar 

  10. Kölsch, M., Turk, M.: Hand Tracking with Flocks of Features. Conference on Computer Vision and Pattern Recognition, CVPR (2005)

    Google Scholar 

  11. Haykin, S.: Adaptive Filter Theory, 4th ed. Pearson Higher Education (2001) ISBN: 0130901261

    Google Scholar 

  12. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd ed. Prentice Hall (2008) ISBN number 9780131687288

    Google Scholar 

  13. Proakis, J. G.: Digital Communications, 4th ed. McGraw-Hill Companies (2001) ISBN: 0072321113

    Google Scholar 

  14. Cant-paz,E., Kamath, R.: ., International Conf. on Simulation of Adaptive Behavior (1993)

    Google Scholar 

  15. Duda, R. O., Hart, P. E., Stork, D.G.: Pattern Classification. John Wiley and Sons (2001)

    Google Scholar 

  16. Hengstler, S., Aghajan, H.: Application Development in Vision-Enabled Wireless Sensor Networks. International Conference on Systems and Networks Communications. ICSNC (2006)

    Google Scholar 

  17. Chen, et. el.: A Low-Bandwidth Wireless Camera Network Platform. Tech. Report, UC Berkeley (2008)

    Google Scholar 

  18. Akyildizand, I., Su, W., Sankarasubramaniam, Y., Cayirci, E.: A survey on sensor networks. IEEE Comm Mag, 40(8) (2002)

    Google Scholar 

  19. Texas Instruments.: TMS320DM647/TMS320DM648 Digital Media Processor. Datasheet Manual (2008)

    Google Scholar 

  20. Marvell Technology Group: PX320 Processor Series. Datasheet Manual (2008)

    Google Scholar 

  21. Iyengar, S.S., Brooks, R.R.: Distributed Sensor Networks. CRC Press (2005) ISBN 1584883839, 9781584883838

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Systems Engineering Department, Naval Postgraduate School, Monterey, CA, U.S.A

    Rachel E. Goshorn

  2. Naval Postgraduate School, MOVES Institute, Monterey, CA, U.S.A

    Deborah E. Goshorn

  3. University of California, San Diego, US

    Deborah E. Goshorn

  4. Computer Science and Engineering Department, La Jolla, CA, U.S.A

    Deborah E. Goshorn

  5. JLG Technologies, Inc., Pebble Beach, CA, U.S.A

    Joshua L. Goshorn & Lawrence A. Goshorn

Authors
  1. Rachel E. Goshorn
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Deborah E. Goshorn
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Joshua L. Goshorn
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lawrence A. Goshorn
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Future University Hakodate, Kameda-Nakano 116-2, Hakodate, Hokkaido, 041-8655, Japan

    Hideyuki Nakashima

  2. Department of Electrical Engineering, Stanford University, 350 Serra Mall, Stanford, CA, 94305-9515, USA

    Hamid Aghajan

  3. School of Computing & Mathematics, University of Ulster at Jordanstown, Shore Road, Newtownabbey, Co. Antrim, UK, BT37 0QB

    Juan Carlos Augusto

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Goshorn, R.E., Goshorn, D.E., Goshorn, J.L., Goshorn, L.A. (2010). Behavior Modeling for Detection, Identification, Prediction, and Reaction (DIPR) in AI Systems Solutions. In: Nakashima, H., Aghajan, H., Augusto, J.C. (eds) Handbook of Ambient Intelligence and Smart Environments. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-93808-0_25

Download citation

  • DOI: https://doi.org/10.1007/978-0-387-93808-0_25

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-93807-3

  • Online ISBN: 978-0-387-93808-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation