Tracking Moving Objects in Road Traffic Sequences

  • Salma Kammoun Jarraya
  • Najla Bouarada Ghrab
  • Mohamed Hammami
  • Hanene Ben-Abdallah
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7340)


In this paper, we present an algorithm for tracking objects in road traffic sequences which is based on coherent strategy. This strategy relies on two times processing. Firstly, a Short-Term Processing (STP) based on spatial analysis and multilevel region descriptors matching allows identification of objects interactions and particular objects states. Secondly, a Long- Term Processing (LTP) is applied to cope with track management issues. In fact LTP feedbacks objects and their corresponding regions in each frame to update tracked object attributes. In case of merging objects, attributes are obtained using Template matching. An experimental study by quantitative and qualitative evaluations shows that the proposed approach can deal with multiple rigid objects whose sizes vary over time. The obtained results prove that our method can provide an effective and stable road objects tracks.


Tracking moving object foreground segmentation point descriptors template matching 


  1. 1.
    Peleshko, D., Ivanov, Y., Kustra, N., Kovalchuk, A.: An application of combined detector algorithm to extract the interest points of foreground objects in videostreams. In: 11th International Conference The Experience of Designing and Application of CAD Systems in Microelectronics, p. 262 (2011)Google Scholar
  2. 2.
    Dan, L., Jian-sheng, Q.: Sift-based object matching and tracking of coal mine. In: IET 3rd International Conference on Wireless, Mobile and Multimedia Networks, pp. 327–330 (2010)Google Scholar
  3. 3.
    Lin, X., Zhang, J., Liu, Z., Shen, J.: Semi-automatic road tracking by template matching and distance transform. In: Joint Urban Remote Sensing Event, pp. 1–7 (2009)Google Scholar
  4. 4.
    Cremers, D., Schnörr, C.: Statistical shape knowledge in variational motion segmentation. Image and Vision Computing 21(1), 77–86 (2003)CrossRefGoogle Scholar
  5. 5.
    Rahman, M., Saha, A., Khanum, S.: Multi-object Tracking in Video Sequences Based on Background Subtraction and SIFT Feature Matching. In: Fourth International Conference on Computer Sciences and Convergence Information Technology, pp. 457–462 (2009)Google Scholar
  6. 6.
    Yan, Y., Wang, J., Li, C.: Object tracking using SIFT features in a particle filter. In: IEEE 3rd International Conference on Communication Software and Networks (ICCSN), pp. 384–388 (2011)Google Scholar
  7. 7.
    Liu, Y., Wang, X., Yang, J., Yao, L.: Multi-objects tracking and online identification based on SIFT. In: International Conference on Multimedia Technology (ICMT), pp. 429–432 (2011)Google Scholar
  8. 8.
    Cheng-bo, Y., Jing, Z., Yu-xuan, L., Ting, Y.: Object tracking in the complex environment based on SIFT. In: 3rd International Conference on Communication Software and Networks, pp. 150–153 (2011)Google Scholar
  9. 9.
    Harris, C., Stephens, M.: A Combined Corner and Edge Detector. In: Alvey Vision Conference, vol. 15(Manchester), pp. 147–151 (1988)Google Scholar
  10. 10.
    Tomasi, C., Kanade, T.: Detection and Tracking of Point Features Technical Report CMU-CS-91-132, pp. 1–22 (1991)Google Scholar
  11. 11.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  12. 12.
    Hammami, M., Jarraya, S., Ben-Abdallah, H.: On line Background Modeling For Moving Object Segmentation in Dynamic Scenes. Multimedia Tools and Applications Journal (available first on-line) (2011)Google Scholar
  13. 13.
    Senior, A., Hampapur, A., Tian, Y.-L., Brown, L., Pankanti, S., Bolle, R.: Appearance Models for Occlusion Handling. In: IEEE Int. Workshop on Performance Evaluation of Tracking and Surveillance (2001)Google Scholar
  14. 14.
    Lisa, M.B., Andrew, W.S., Tian, Y.-L., Connell, J., Hampapur, A.: Performance Evaluation of Surveillance Systems Under Varying Conditions. In: IEEE Int. Workshop on Performance Evaluation of Tracking and Surveillance (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Salma Kammoun Jarraya
    • 1
  • Najla Bouarada Ghrab
    • 1
  • Mohamed Hammami
    • 2
  • Hanene Ben-Abdallah
    • 1
  1. 1.MIRACL-FSEGSfax UniversitySfaxTunisia
  2. 2.MIRACL-FSSfax UniversitySfaxTunisia

Personalised recommendations