Skip to main content

Object’s Interaction Management by Means of a Fuzzy System within a Context-Based Tracking System

  • Conference paper
International Symposium on Distributed Computing and Artificial Intelligence 2008 (DCAI 2008)

Part of the book series: Advances in Soft Computing ((AINSC,volume 50))

  • 1281 Accesses

Summary

Tracking objects through interactions is a complex task, especially when it is important to be able to obtain the final trajectory followed by the object being track. This work proposes the use of a Context Layer to solve the problem of tracking through objects interactions, using a Fuzzy Reasoning System. Other authors have already used context information within a tracking system in order to improve its performance. The novelty of this work relies in that a Context Layer is created to reason over a general tracking system and thus improve the performance, instead of creating a context-based tracking algorithm. The experimentation shows how this Context Layer reasons over and improves a general tracking system.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Tian, W., Zhang, B., Jin, Z.: Joint tracking algorithm using particle filter and mean shift with target model updating. Chinese Optics Letters 4(10), 569–572 (2006)

    Google Scholar 

  2. Brdiczka, O., Yuen, P.C., Zaidenberg, S., Reignier, P., Crowley, J.L.: Automatic acquisition of context models and its application to video surveillance. In: Proceedings of the 18th International Conference on Pattern Recognition (ICPR 2006) (2006)

    Google Scholar 

  3. Dey, A.K.: Understanding and using context. J. Personal and Ubiquitous Computing 5(1) (February 2001)

    Google Scholar 

  4. Bremond, F., Cupillard, F., Thonnat, M.: Tracking groups of people for video surveillance. In: Proc. of the 2nd European Workshop on Advanced Video-Based Surveillance System (2001)

    Google Scholar 

  5. Morency, L., Sidner, C., Lee, C., Darrell, T.: Contextual recognition of head gestures. In: Proc. of ICMI (2005)

    Google Scholar 

  6. Angel Patricio, M., Garcia, J., Berlanga, A., Manuel Molina, J.: Video tracking association problem using estimation of distribution algorithms in complex scenes. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2007. LNCS, vol. 4528, pp. 261–270. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  7. Patricio, M.A., Castanedo, F., Berlanga, A., Perez, O., Garcia, J., Molina, J.M.: Computational Intelligence in Visual Sensor Networks: Improving Video Processing Systems. In: Computational Intelligence in Multimedia Processing: Recent Advances. Studies in Computational Intelligence, vol. 96, pp. 351–377. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  8. Patricio, M.A., Garcia, J., Berlanga, A., Molina, J.M.: Solving video-association problem with explicit evaluation of hypothesis using edas. In: Proceedings of the 2008 Congress on Evolutionary Computation CEC 2008. IEEE Press, Los Alamitos (2008)

    Google Scholar 

  9. Polana, R., Nelson, R.: Low level recognition of human motion. In: Motion of Non-Rigid and Articulated Objects, pp. 77–82 (1994)

    Google Scholar 

  10. Sanchez, A.M., Patricio, M.A., Garcia, J., Molina, J.M.: Video tracking improvement using context-based information. In: The 10th International Conference on Information Fusion, Quebec (July 2007)

    Google Scholar 

  11. Stauffer, C.: Estimating tracking sources and sinks. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, Madison, WI, pp. 259–266 (July 2003)

    Google Scholar 

  12. Pan, Q., Yang, T., Li, S.Z., Li, J.: Real-time multiple objects tracking with occlusion handling in dynamic scenes. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005) (2005)

    Google Scholar 

  13. Torralba, A., Murphy, K.P., Freeman, W.T., Rubin, M.A.: Context-based vision system for place and object recognition. In: Proc. of ICCV (2003)

    Google Scholar 

  14. Xu, M., Ellis, T.: Augmented tracking with incomplete observation and probabilistic reasoning. Image and Vision Computing 24, 1202–1217 (2006)

    Article  Google Scholar 

  15. Zhao, T., Nevatia, R.: Tracking multiple humans in complex situations. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(9), 1208–1221 (2004)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Juan M. Corchado Sara Rodríguez James Llinas José M. Molina

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Sánchez, A.M., Patricio, M.A., García, J. (2009). Object’s Interaction Management by Means of a Fuzzy System within a Context-Based Tracking System. In: Corchado, J.M., Rodríguez, S., Llinas, J., Molina, J.M. (eds) International Symposium on Distributed Computing and Artificial Intelligence 2008 (DCAI 2008). Advances in Soft Computing, vol 50. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85863-8_85

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-85863-8_85

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-85862-1

  • Online ISBN: 978-3-540-85863-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics