Improving the Segmentation Stage of a Pedestrian Tracking Video-Based System by Means of Evolution Strategies

  • O. Pérez
  • M. Á. Patricio
  • J. García
  • J. M. Molina
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3907)


Pedestrian tracking video-based systems present particular problems such as the multi fragmentation or low level of compactness of the resultant blobs due to the human shape or movements. This paper shows how to improve the segmentation stage of a video surveillance system by adding morphological post-processing operations so that the subsequent blocks increase their performance. The adjustment of the parameters that regulate the new morphological processes is tuned by means of Evolution Strategies. Finally, the paper proposes a group of metrics to assess the global performance of the surveillance system. After the evaluation over a high number of video sequences, the results show that the shape of the tracks match up more accurately with the parts of interests. Thus, the improvement of segmentation stage facilitates the subsequent stages so that global performance of the surveillance system increases.


Surveillance System Video Sequence Segmentation Stage Morphological Operator Visual Surveillance 
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.
    Përez, O., García, J., Berlanga, A., Molina, J.M.: Evolving Parameters of Surveillance Video System for Non-Overfitted Learning. In: Proc. 7th European Workshop on Evolutionary Computation in Image Analysis and Signal Processing EvoIASP 2005, Lausanne, Switzerland (2005)Google Scholar
  2. 2.
    García, J.A., Besada, J.M., Molina, J.: Fuzzy data association for imagebased tracking in dense scenarios. In: IEEE International Conference on Fuzzy Systems, Honolulu, Hawaii (2002)Google Scholar
  3. 3.
    Friedman, N., Russell, S.: Image segmentation in video sequences: A probabilistic approach. In: Proceedings of the Thirteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI 1997), pp. 175–181. Morgan Kaufmann Publishers, Inc., San Francisco (1997)Google Scholar
  4. 4.
    Rosin, P.L., Ellis, T.: Image Difference Threshold Strategies and Shadow Detection. In: The 6th BMVC 1995 conf. proc., Birmingham, UK, pp. 347–356 (1995)Google Scholar
  5. 5.
    Jiang, C.: Shadow identification. CVGIP: Image Understanding 59(2), 213–225 (1994)CrossRefGoogle Scholar
  6. 6.
    Prati, A., Mikic, I., Trivedi, M.M., Cucchiara, R.: Detecting Moving Shadows: Algorithms and Evaluation. IEEE Trans. PAMI 25(7), 918–923 (2003)Google Scholar
  7. 7.
    Bevilacqua, A.: Effective Shadow Detection in Traffic Monitoring Applications. In: WSCG 2003, vol. 11(1) (2003)Google Scholar
  8. 8.
    Zhang, Y.J.: Evaluation and comparison of different segmentation algorithms. Pattern Recognition Letters 18, 963–974 (1997)CrossRefGoogle Scholar
  9. 9.
    Chabrier, S., Emile, B., Laurent, H., Rosenberger, C., March, P.: Unsupervised Evaluation of Image Segmentation Application to Multi-spectral Images. In: 17th International Conference on Pattern Recognition (ICPR 2004), vol. 1, pp. 576–579 (2004)Google Scholar
  10. 10.
    Erdem, E., Sankur, B., Tekalp, A.M.: Metrics for performance evaluation of video object segmentation and tracking without ground-truth. ICIP 2, 69–72 (2001)Google Scholar
  11. 11.
    Pokrajac, D., Latecki, L.J.: Spatiotemporal Blocks-Based Moving Objects Identification and Tracking. In: IEEE Int. W. Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS), Nice, France (2003)Google Scholar
  12. 12.
    Black, J., Ellis, T., Rosin, P.: A Novel Method for Video Tracking Performance Evaluation. In: Joint IEEE Int. Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS), Nice, France (2003)Google Scholar
  13. 13.
    Aggarwal, J.K., Cai, Q.: Human motion analysis: A review. Computer Vision and Image Understanding 73(3), 428–440 (1999)CrossRefGoogle Scholar
  14. 14.
    Wang, L., Hu, W., Tan, T.: Recent developments in human motion analysis. Pattern Recognition 36(3), 585–601 (2003)CrossRefGoogle Scholar
  15. 15.
    Haritaoglu, D.I., Harwood, D., Davis, L.: W4: Real- Time Surveillance of People and Their Activities. IEEE Trans. Pattern Analysis and Machine Intelligence 22(8), 809–830 (2000)CrossRefGoogle Scholar
  16. 16.
    Remagnino, P., Jones, G.A., Paragios, N., Regazzoni, C.S.: Video-Based Surveillance Systems. Kluwer Academic Publishers, Dordrecht (2002)Google Scholar
  17. 17.
    Wren, C., Azarbayejani, A., Darrell, T., Pentland, A.P.: Pfinder: Real-time Tracking of the Human Body. IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI) 19(7), 780–785 (1997)CrossRefGoogle Scholar
  18. 18.
    Stauffer, C., Grimson, W.: Adaptive background mixture models for realtime tracking. In: Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 246–252 (1999)Google Scholar
  19. 19.
    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
  20. 20.
    Cohen, I., Medioni, G.: Detecting and Tracking Moving Objects in Video from an Airborne Observer. In: Proc. IEEE Image Understanding Workshop, pp. 217–222 (1998)Google Scholar
  21. 21.
    Kim, E.Y., Park, S.H., Hwang, S., Kim, H.J.: Video Sequence Segmentation Using Genetic Algorithms. Pattern Recognition Letter 23(7), 843–863 (2002)MATHCrossRefGoogle Scholar
  22. 22.
    Hwang, S., Kim, E.Y., Park, S.H., Kim, H.J.: Object Extraction and Tracking Using Genetic Algorithms. In: Proc. IEEE Signal Processing Society ICIP, vol. 2, pp. 383–386 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • O. Pérez
    • 1
  • M. Á. Patricio
    • 1
  • J. García
    • 1
  • J. M. Molina
    • 1
  1. 1.Computer DepartmentUniversidad Carlos III de MadridColmenarejo MadridSpain

Personalised recommendations