Advertisement

Robust Vehicle Blob Tracking with Split/Merge Handling

  • Xuefeng Song
  • Ram Nevatia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4122)

Abstract

Evaluation results of a vehicle tracking system on a given set of evaluation videos of a street surveillance system are presented. The method largely depends on detection of motion by comparison with a learned background model. Several difficulties of the task are overcome by the use of general constrains of scene, camera and vehicle models. An analysis of results is also presented.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kasturi, R., Goldgof, D., Soundararajan, P., Manohar, V., Boonstra, M., Korzhova, V.: Performance Evaluation Protocal for Face, Person and Vehicle Detection & Tracking in Video Analysis and Centent Extraction (VACE-II). In: CLEAR - Classification of Events, Activities and Relationships (2006), http://www.nist.gov/speech/tests/clear/2006/CLEAR06-R106-EvalDiscDoc/DataandInformation/ClearEval_Protocol_v5.pdf
  2. 2.
    Li, L., Huang, W., Gu, I.Y.H., Tian, Q.: Foreground Object Detection from Videos Containing Complex Background. ACM MM (2003)Google Scholar
  3. 3.
    Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: IEEE Conf. on Computer Vision and Pattern Recognition 2001, vol.1, pp. 511–518 (2001)Google Scholar
  4. 4.
    Lv, F., Zhao, T., Nevatia, R.: Self-Calibration of a Camera from Video of a Walking Human. In: 16th International Conference on Pattern Recognition (ICPR), Quebec, Canada (2002)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Xuefeng Song
    • 1
  • Ram Nevatia
    • 1
  1. 1.Univ. of Southern California, Los Angeles, CA 90089USA

Personalised recommendations