Modelling Object Behaviour in a Video Surveillance System Using Pawlak’s Flowgraph

  • Karol Lisowski
  • Andrzej Czyzewski
Part of the Communications in Computer and Information Science book series (CCIS, volume 429)


In this paper, methodology of acquisition and processing of video streams for the purpose of modelling object behaviour is presented. Multilevel contextual video processing was also mentioned. The Pawlak’s flowgraph is used as a container for the knowledge related to the behaviour of objects in the area supervised by a video surveillance system. Spatio-temporal dependencies in transitions between cameras can be easily changed in real-life situations. In order to cope with such fluctuating conditions, an adaptive algorithm is implemented. Consequently, as it was shown the flowgraph reacts faster to the occurring changes.


surveillance systems Pawlak’s fowgraphs object behaviour 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cai, Y., Chen, W., Huang, K., Tan, T.: Continuously tracking objects across multiple widely separated cameras. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007, Part I. LNCS, vol. 4843, pp. 843–852. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Colombo, A., Orwell, J., Velastin, S.: Colour constancy techniques for re-recognition of pedestrians from multiple surveillance cameras. In: Workshop on Multi-Camera and Multi-Modal Sensor Fusion Algorithms and Applications (2008)Google Scholar
  3. 3.
    Dalka, P., Czyżewski, A.: Vehicle classification based on soft computing algorithms. In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds.) RSCTC 2010. LNCS, vol. 6086, pp. 70–79. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Czyżewski, A., Kostek, B.: Musical metadata retrieval with flow graphs. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 691–698. Springer, Heidelberg (2004), CrossRefGoogle Scholar
  5. 5.
    Czyzewski, A., Lisowski, K.: Adaptive method of adjusting flowgraph for route reconstruction in video surveillance systems. Fundamenta Informaticae 127, 561–576 (2013)MathSciNetGoogle Scholar
  6. 6.
    Czyzewski, A., Lisowski, K.: Employing flowgraphs for forward route reconstruction in video surveillance system. Journal of Intelligent Information Systems, 1–15 (2013),
  7. 7.
    Dalka, P., Szwoch, G., Szczuko, P., Czyzewski, A.: Video content analysis in the urban area telemonitoring system. In: Tsihrintzis, G.A., Jain, L.C. (eds.) Multimedia Services in Inteligent Environments. SIST, vol. 3, pp. 241–261. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Dunin-Keplicz, B., Jankowski, A., Skowron, A., Szczuka, M., Pawlak, Z.: Flow graphs, their fusion and data analysis. In: Monitoring, Security, and Rescue Techniques in Multiagent Systems. Advances in Soft Computing, vol. 28, pp. 3–12. Springer, Heidelberg (2005),, 10.1007Google Scholar
  9. 9.
    Ellwart, D., Czyżewski, A.: Visual objects description for their re-identification in multi-camera systems. In: Zgrzywa, A., Choroś, K., Siemiński, A. (eds.) Multimedia and Internet Systems: Theory and Practice. AISC, vol. 183, pp. 45–54. Springer, Heidelberg (2013), CrossRefGoogle Scholar
  10. 10.
    Farrell, R., Davis, L.S.: Decentralized discovery of camera network topology. IEEE (2008)Google Scholar
  11. 11.
    Gilbert, A., Bowden, R.: Tracking objects across cameras by incrementally learning inter-camera colour calibration and patterns of activity. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 125–136. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  12. 12.
    Greco, S., Pawlak, Z., Słowiński, R.: Generalized decision algorithms, rough inference rules, and flow graphs. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 93–104. Springer, Heidelberg (2002), CrossRefGoogle Scholar
  13. 13.
    Javed, O.: Appearance modeling for tracking in multiple non-overlapping cameras. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 26–33 (2005)Google Scholar
  14. 14.
    Leung, V., Orwell, J., Velastin, S.: Performance evaluation of tracking for public transport surveillance. Annals of the BMVA 2010, 1–12 (2010)Google Scholar
  15. 15.
    Liu, H., Sun, J., Zhang, H.: Interpretation of extended pawlak flow graphs using granular computing. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets VIII. LNCS, vol. 5084, pp. 93–115. Springer, Heidelberg (2008), CrossRefGoogle Scholar
  16. 16.
    Matsumoto, M., Nishimura, T.: Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans. Model. Comput. Simul. 8(1), 3–30 (1998), CrossRefMATHGoogle Scholar
  17. 17.
    Nama, Y., Ryu, J., Choi, Y., Cho, W.: Learning spatio-temporal topology of a multi-camera network by tracking multiple people. World Academy of Science - Engieneering and Technology (2007)Google Scholar
  18. 18.
    Niu, C., Grimson, E.: Recovering non-overlapping network topology using far-field vehicle tracking data. In: The 18th International Conference on Pattern Recognition, ICPR 2006 (2006)Google Scholar
  19. 19.
    Pawlak, Z.: Flow graphs and data mining. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol. 3400, pp. 1–36. Springer, Heidelberg (2005), CrossRefGoogle Scholar
  20. 20.
    Pawlak, Z.: Rough sets and flow graphs. In: Ślęzak, D., Wang, G., Szczuka, M.S., Düntsch, I., Yao, Y. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3641, pp. 1–11. Springer, Heidelberg (2005), CrossRefGoogle Scholar
  21. 21.
    Pawlak, Z.: Some remarks on conflict analysis. European Journal of Operational Research 166, 649–654 (2005)CrossRefMATHGoogle Scholar
  22. 22.
    Pawlak, Z.: Conflicts and negotations. In: Wang, G.-Y., Peters, J.F., Skowron, A., Yao, Y. (eds.) RSKT 2006. LNCS (LNAI), vol. 4062, pp. 12–27. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  23. 23.
    Sun, J., Liu, H., Zhang, H.: An extension of pawlak’s flow graphs. In: Wang, G.-Y., Peters, J.F., Skowron, A., Yao, Y. (eds.) RSKT 2006. LNCS (LNAI), vol. 4062, pp. 191–199. Springer, Heidelberg (2006), CrossRefGoogle Scholar
  24. 24.
    Szwoch, G.: Performance evaluation of the parallel codebook algorithm for background subtraction in video stream. In: Dziech, A., Czyżewski, A. (eds.) MCSS 2011. CCIS, vol. 149, pp. 149–157. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  25. 25.
    Szwoch, G., Dalka, P., Czyzewski, A.: Objects classification based on their physical sizes for detection of events in camera images. In: NTAV/SPA 2008 Signal Processing: Algorithms, Architectures, Arrangements, and Applications; New Trends in Audio and Video, pp. 15–20 (2008)Google Scholar
  26. 26.
    Szwoch, G., Dalka, P., Czyzewski, A.: Resolving conflicts in object tracking for automatic detection of events in video. Elektronika (2011)Google Scholar
  27. 27.
    Tieu, K., Dalley, G., Grimson, W.E.L.: Inference of non-overlapping camera network topology by measuring statistical dependence. In: Proceedings of the Tenth IEEE International Conference on Computer Vision, ICCV 2005 (2005)Google Scholar
  28. 28.
    Ou Yang, Y.-P., Shieh, H.-M., Tzeng, G.-H., Yen, L., Chan, C.-C.: Business aviation decision-making using rough sets. In: Chan, C.-C., Grzymala-Busse, J.W., Ziarko, W.P. (eds.) RSCTC 2008. LNCS (LNAI), vol. 5306, pp. 329–338. Springer, Heidelberg (2008), CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Karol Lisowski
    • 1
  • Andrzej Czyzewski
    • 1
  1. 1.Department of Multimedia SystemsGdańsk University of TechnologyGdañskPoland

Personalised recommendations