Objects Detection and Tracking in Highly Congested Traffic Using Compressed Video Sequences

  • Marcin Bernaś
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7594)


The paper presents a model to detect and track vehicles in highly congested traffic using low quality (usually compressed) video sequences. Robustness of the model is provided by applying a data fusion for various detection and tracking algorithms. The surveys to find reliable detection algorithms were performed. Basing on the experiments, the model calibration and results were presented. The proposed model provides data, which can be used by traffic engineers in various microscopic traffic simulations.


Video Sequence Background Subtraction Object Detection Object Tracking Vehicle Detection 
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.
    Yilmaz, A., Javed, O., Shah, M.: Object Tracking: A Survey. ACM Computing Surveys 38(4), Article 13 (2006)Google Scholar
  2. 2.
    Jeyakar, J., Babu, V., Ramakrishnan, K.R.: Robust object tracking with background-weighted local kernels. Computer Vision and Image Understanding 112, 296–309 (2008)CrossRefGoogle Scholar
  3. 3.
    Yun, X.P., Bachmann, E.R.: Design, Implementation, and Experimental Results of a Quaternion-Based Kalman Filter for Human Body Motion Tracking. IEEE Transactions on Robotics 22, 1216–1227 (2006)CrossRefGoogle Scholar
  4. 4.
    Jwo, D.J., Wang, S.H.: Adapative Fuzzy Strong Tracking Extended Kalman Filtering for GPS Navigation. IEEE Sensors Journal 7, 778–789 (2007)CrossRefGoogle Scholar
  5. 5.
    Cho, J.U., Jin, S.H., Pham, X.D., Jeon, J.W., Byun, J.E., Kang, H.: A Real-Time Object Tracking System Using a Particle Filter. In: IEEE International Conference on Intelligent Robots and Systems, pp. 2822–2827. IEEE Press, Beijing (2006)Google Scholar
  6. 6.
    Pamula, W.: Determining Feature Points for Classification of Vehicles. In: Burduk, R., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds.) Computer Recognition Systems 4. AISC, vol. 95, pp. 677–684. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Pamula, W.: Feature Extraction Using Reconfigurable Hardware. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2010, Part II. LNCS, vol. 6375, pp. 158–165. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Hesse, C.W., James, C.J.: The Fast ICA Algorithm with Spatial Constraints. IEEE Signal Processing Letters 12, 792–795 (2005)CrossRefGoogle Scholar
  9. 9.
    Kyungnam, K., Harwood, D., Davis, L.: Real-time foreground-background segmentation using codebook model. Real-Time Imaging Journal 11(3) (2005)Google Scholar
  10. 10.
    Pan, J.Y., Hu, B., Zhang, J.Q.: Robust and Accurate Object Tracking under Various Types of Occlusions. IEEE Transaction on Circuits and Systems for Video Technology 18, 223–236 (2008)CrossRefGoogle Scholar
  11. 11.
    Hyvarinene, A.: Fast and Robust Fixed-point Algorithms for Independent Component Analysis. IEEE Transactions on Neural Networks 10, 626–634 (1999)CrossRefGoogle Scholar
  12. 12.
    Jayabalan, E., Krishnan, A.: Detection and Tracking of Moving Object in Compressed Videos. In: Das, V.V., Stephen, J., Chaba, Y. (eds.) CNC 2011. CCIS, vol. 142, pp. 39–43. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  13. 13.
    Chen, Y., Rui, Y.: Real Time Object Tracking in Video Sequences. In: Signals and Communication Technology, Part II, pp. 67–88 (2006)Google Scholar
  14. 14.
    Bajaj, P.R., Daigavane, M.B.: Vehicle Detection and Neural Network Application for Vehicle Classification. In: Proc. of Computational Intelligence and Communication Networks (CICN), pp. 758–762 (2011)Google Scholar
  15. 15.
    Placzek, B.: Fuzzy Cellular Model for On-Line Traffic Simulation. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Wasniewski, J. (eds.) PPAM 2009, Part II. LNCS, vol. 6068, pp. 553–560. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  16. 16.
    Płaczek, B.: A Real Time Vehicle Detection Algorithm for Vision-Based Sensors. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2010, Part II. LNCS, vol. 6375, pp. 211–218. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Marcin Bernaś
    • 1
  1. 1.Faculty of TransportSilesian University of TechnologyKatowicePoland

Personalised recommendations