Advertisement

Vehicle Tracking Using a Multi-scale Bayesian Algorithm for a Perspective Image of a Road

  • Przemysław Mazurek
  • Krzysztof Okarma
Part of the Communications in Computer and Information Science book series (CCIS, volume 239)

Abstract

Tracking the vehicles in the short distance from the camera using the perspective view of the camera installed above the road requires considering the effect of perspective. Vehicles that are close to the camera are large and easily distinguishable on individual video frames. Moreover, the separation between adjacent vehicles is also high. However, tracking capabilities are deteriorated for longer distances of the vehicle from the camera. The article presents a solution for tracking the vehicles moving away from the camera, based on the image analysis at different scales in order to increase the range of the tracking system. The proposed algorithm utilizes a block matching technique using the correlation coefficients but the size of matched blocks varies for different video frames. Matching can be implemented in several variations depending on the choice of the reference blocks only in the previous frame or averaging of several frames. A particularly useful technique, used in the paper, is the spatio-temporal recursive Track-Before-Detect algorithm, especially for distant objects represented by a small number of pixels.

Keywords

Track-Before-Detect Multi-scale video tracking Intelligent Transport Systems 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bar–Shalom, Y.: Multitarget–Multisensor Tracking: Applications and Advances, vol. II. Artech House, Norwood (1992)Google Scholar
  2. 2.
    Blackman, S., Poupoli, R.: Modern Tracking Systems. Artech House, Norwood (1999)Google Scholar
  3. 3.
    Blackman, S.: Multiple–Target Tracking with Radar Applications. Artech House, Norwood (1986)Google Scholar
  4. 4.
    Boers, Y., Ehlers, F., Koch, W., Luginbuhl, T., Stone, L.D., Streit, R.L. (eds.): Track Before Detect Algorithm, EURASIP Journal on Advances in Signal Processing, Hindawi (2008)Google Scholar
  5. 5.
    Klein, L.A.: Sensor Technologies and Data Requirements for ITS. Artech House ITS library, Norwood (2001)Google Scholar
  6. 6.
    Mazurek, P.: Suppression of Impulse Noise in Track-Before-Detect Algorithms. Computer Applications in Electrical Engineering 8, 201–211 (2010)Google Scholar
  7. 7.
    Okarma, K.: Combined Full-Reference Image Quality Metric Linearly Correlated with Subjective Assessment. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010. LNCS (LNAI), vol. 6113, pp. 539–546. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Stone, L.D., Barlow, C.A., Corwin, T.L.: Bayesian Multiple Target Tracking. Artech House, Norwood (1999)zbMATHGoogle Scholar
  9. 9.
    Traffic Detector Handbook: Third Edition, Publication No. FHWA-HRT-06-10, Federal Highway Administration, vol. I, US Department of Transportation (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Przemysław Mazurek
    • 1
  • Krzysztof Okarma
    • 1
  1. 1.Higher School of Technology and Economics in Szczecin Faculty of Motor TransportSzczecinPoland

Personalised recommendations