Advertisement

Multi-agent System for Tracking and Classification of Moving Objects

  • Sergio SánchezEmail author
  • Sara Rodríguez
  • Fernando De la Prieta
  • Juan F. De Paz
  • Javier Bajo
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 373)

Abstract

In the past, computational barriers have limited the complexity of video and image processing applications but recently, faster computers have enabled researchers to consider more complex algorithms which can deal successfully with vehicle and pedestrian detection technologies. However, much of the work only pays attention to the accuracy of the final results provided by the systems, leaving aside the computational efficiency. Therefore, this paper describes a system using a paradigm of multi-agent system capable of regulating itself dynamically taking into account certain parameters pertaining to detection, tracking and classification, to reduce the computational burden as low as possible at all times without this in any way compromise the reliability of the result.

Keywords

Computer Vision Agents Multi-Agent System Vehicle Detection Vehicle Counting Pedestrian Detection Classifiers Video-Surveillance 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Baker, S., Roth, S., Scharstein, D., Black, M.J., Lewis, J.P., Szeliski, R.: A Database and Evaluation Methodology for Optical Flow. In: IEEE 11th International Conference on Computer Vision, ICCV 2007, pp. 1–8 (2007), doi:10.1109/ICCV.2007.4408903, ISSN 1550-5499Google Scholar
  2. 2.
    Bellifemine, F.L., Poggi, A., Rimassa, G.: Developing multi-agent systems with JADE. In: Castelfranchi, C., Lespérance, Y. (eds.) ATAL 2000. LNCS (LNAI), vol. 1986, pp. 89–103. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  3. 3.
    Benezeth, Y., Jodoin, P.-M., Emile, B., Haurent, H., Rosenberg, C.: Comparative Study of Background Subtraction Algorithms. Journal of Electronic Imaging 19 (2012)Google Scholar
  4. 4.
    Boissier, O., Demazeau, Y.A.: An architecture for social and invidivual control and its application to Computer Vision. In: European Workshop on Modeling Autonomous Agents In a Multiagent World, pp. 107–118 (1994)Google Scholar
  5. 5.
    Brooks, R.A.: Intelligence without representation. Artificial Intelligence 47, 139–159 (1991)CrossRefGoogle Scholar
  6. 6.
    Liu, Y.H., Wang, S.Z., Du, X.M.: A multi-agent information fusion model for ship collision avoidance. In: Proceedings of the International Conference on Machine Learning and Cybernetics, pp. 6–11 (2008)Google Scholar
  7. 7.
    Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proc. of the 7th IJCAI, Vancouver, Canada, pp. 674–679 (1981)Google Scholar
  8. 8.
    Luo, H., Yang, S., Hu, X.: Agent oriented intelligent fault diagnosis system using evidence theory. Expert Systems with Applications 39(3), 2524–2531 (2012)CrossRefGoogle Scholar
  9. 9.
    Sundaram, N.: Making computer vision computationally efficient. EECS Department University of California, Berkeley Technical Report No. UCB/EECS-2012-106 (May 11, 2012)Google Scholar
  10. 10.
    Piccardi, M.: Background subtraction techniques: a review. In: 2004 IEEE International Conference on Systems, Man and Cybernetics (2004)Google Scholar
  11. 11.
    Zhu, Q., Avidan, S., Yeh, M.-C., Cheng, K.-T.: Fast Human Detection Using a Cascade of Histograms of Oriented Gradients. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1491–1498 (2006), doi:10.1109/CVPR.2006.119, ISSN:1063-6919Google Scholar
  12. 12.
    Rodríguez, S., De Paz, Y., Bajo, J., Corchado, J.M.: Social-based planning model for multiagent systems. Expert Systems with Applications 38(10), 13005–13023 (2011)CrossRefGoogle Scholar
  13. 13.
    Rodríguez, S., De la Prieta, F., García, E., Zato, C., Bajo, J., Corchado, J.M.: Virtual Organizations in Information Fusion. In: 9th International Conference on Practical Applications of Agents and Multiagent Systems - Special Session on Adaptive Multiagent Systems, Salamanca, Spain, pp. 195–202 (2011)Google Scholar
  14. 14.
    Shi, J., Tomasi, C.: Good features to track. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 1994), Seattle (June 1994)Google Scholar
  15. 15.
    Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2 (1999), doi:10.1109/CVPR.1999.784637, ISSN:1063-6919Google Scholar
  16. 16.
    Tapia, D.I., de la Prieta, F., Rodríguez González, S., Bajo, J., Corchado, J.M.: Organizations of Agents in Information Fusion Environments. In: Antunes, L., Pinto, H.S. (eds.) EPIA 2011. LNCS, vol. 7026, pp. 59–70. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  17. 17.
    Viola, P., Jones, M., Snow, D.: Detecting pedestrians using patterns of motion and appearance (2003)Google Scholar
  18. 18.
    Wang, X.: Intelligent multi-camera video surveillance: A review. Pattern Recognition Letters 34(1), 3–19 (2013), doi:10.1016/j.patrec.2012.07.005CrossRefGoogle Scholar
  19. 19.
    Zato, C., Sanchez, A., Villarrubia, G., Rodriguez, S., Corchado, J.M., Bajo, J.: Platform for building large-scale agent-based systems. In: 2012 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), pp. 17–18 (May 2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sergio Sánchez
    • 1
    Email author
  • Sara Rodríguez
    • 1
  • Fernando De la Prieta
    • 1
  • Juan F. De Paz
    • 1
  • Javier Bajo
    • 2
  1. 1.Computer and Automation DepartmentUniversity of SalamancaSalamancaSpain
  2. 2.Artificial Intelligence DepartmentPolytechnic University of MadridMadridSpain

Personalised recommendations