Segmentation of Moving Objects with Information Feedback Between Description Levels

  • M. Rincón
  • E. J. Carmona
  • M. Bachiller
  • E. Folgado
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4528)


In real sequences, one of the factors that most negatively affects the segmentation process result is the existence of scene noise. This impairs object segmentation which has to be corrected if we wish to have some minimum guarantees of success in the following tracking or classification stages. In this work we propose a generic knowledge-based model to improve the segmentation process. Specifically, the model uses a decomposition strategy in description levels to enable the feedback of information between adjacent levels. Finally, two case studies are proposed that instantiate the model proposed for detecting humans.


Human Model Video Surveillance Segmentation Process Information Feedback Object Level 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Horprasert, T., Harwood, D., Davis, L.S.: A statistical approach for realtime robust background subtraction and shadow detection. In: Proc. of IEEE Frame Rate Workshop, Kerkyra, Greece, pp. 1–19 (1999)Google Scholar
  2. 2.
    Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 246–252 (1999)Google Scholar
  3. 3.
    Lipton, A.J., Fujiyoshi, H., Patil, R.S.: Moving target classification and tracking from real-time video. In: Proc. of Workshop Applications of Computer Vision, pp. 129–136 (1998)Google Scholar
  4. 4.
    Wang, L., Hu, W., Tan, T.: Recent developments in human motion analysis. Pattern Recognition 36(3), 585–601 (2003)CrossRefGoogle Scholar
  5. 5.
    Kim, E.Y., Park, S.H.: Automatic video segmentation using genetic algorithms. Pattern Recognition Letters 27(11), 1252–1265 (2006)CrossRefGoogle Scholar
  6. 6.
    Carmona, E.J., Martínez-Cantos, J., Mira, J.: Posprocesamiento morfológico adaptativo basado en algoritmos genéticos y orientado a la detección robusta de humanos. In: Campus Multidisciplinary in Perception and Intelligence, CMPI-2006, Albacete, Spain, July 2006, pp. 249–261 (2006)Google Scholar
  7. 7.
    Martínez-Cantos, J., Carmona, E.J., Fernández-Caballero, A., López, M.T.: Mejora paramétrica de la interacción lateral en computación acumulativa. In: Campus Multidisciplinary in Perception and Intelligence, CMPI-2006, Albacete, Spain, July 2006, pp. 262–273 (2006)Google Scholar
  8. 8.
    Collins, R.T., Lipton, A.J., Kanade, T.: Special Issue on Video Surveillance. IEEE Trans. Pattern Anal. Mach. Intell. 22(8) (2000)Google Scholar
  9. 9.
    Dedeoglu, Y.: Moving Object Detection, Tracking and Classification for Smart Video Surveillance. Ph.D. Thesis (2004)Google Scholar
  10. 10.
    Haritaoglu, I., Harwood, D., Davis, L.S.: W4: Real-time Surveillance of People and Their Activities. PAMI 22(8), 809–830 (2000)Google Scholar
  11. 11.
    Stauffer, C., Grimson, W.: Learning Patterns of Activity Using Real-Time Tracking. IEEE Trans. Pattern Analysis and MachineIntelligence 22(8), 747–757 (2000)CrossRefGoogle Scholar
  12. 12.
    Cucchiara, R., Piccardi, M., Prati, A.: Detecting Moving Objects, Ghost and Shadows in Video Streams. IEEE Trans. Pattern Analysis and Machine Intelligence 25(10), 1337–1342 (2003)CrossRefGoogle Scholar
  13. 13.
    Carmona, E.J., Martínez-Cantos, J., Mira, J.: A new video segmentation method of mobile objects based on blob-level knowledge. Pattern Recognition Letters, under review (2007)Google Scholar
  14. 14.
    Dillon, C., Caelli, T.: Learning Image Annotation: the CITE system. Videre 1(2), 90–123 (1998)Google Scholar
  15. 15.
    Sing, S., Sowmya, A.: RAIL: Road Recognition from Aerial Images Using Inductive Learning. International Archives of Photogrammetry and Remote Sensing 32(3/1), 367–378 (1998)Google Scholar
  16. 16.
    Tani, J.: Model-based learning for mobile robot navigation from the dynamical systems perspective. IEEE Transactions on Systems, Man and Cybernetics, Part B 26(3), 421–436 (1996)CrossRefGoogle Scholar
  17. 17.
    Nagel, H.H.: Steps toward a cognitive vision system. AI Mag. 25(2), 31–50 (2004)MathSciNetGoogle Scholar
  18. 18.
    Folgado, E., Rincón, M., Carmona, E.J., Bachiller, M.: A block-based model for monitoring of human activity. IEEE trans. on Pattern Analysis and Machine Intelligence, under review (2007)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • M. Rincón
    • 1
  • E. J. Carmona
    • 1
  • M. Bachiller
    • 1
  • E. Folgado
    • 1
  1. 1.Dpto. de Inteligencia Artificial. ETSI Informatica. UNED., Juan del Rosal 16, 28040 MadridSpain

Personalised recommendations