Pattern Recognition and Image Analysis

, Volume 18, Issue 3, pp 400–405 | Cite as

Automatic traffic monitoring based on aerial image sequences

  • D. Lenhart
  • S. Hinz
  • J. Leitloff
  • U. Stilla
Application Problems


Traffic monitoring is a very important task in today’s traffic control and flow management. The acquisition of traffic data in almost real-time is essential to swiftly react to current situations. Stationary data collectors such as induction loops and video cameras mounted on bridges or traffic lights are matured methods. The latter have been thoroughly studied for instance in [1, 2], and in [5, 9] even for moving cameras. However, they deliver only local data and are not able to observe the global traffic situation. Spaceborne sensors do cover very large areas. Because of their relatively short acquisition time and their long revisit period, such systems contribute to the periodic collection of statistical traffic data to validate and improve certain traffic models.


Road Segment Short Acquisition Time Vehicle Detection Induction Loop Vehicle Tracking 
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.
    L. Dreschler and H.-H. Nagel, “Volumetric Model and Trajectory of a Moving Car Derived from Monocular TV Frame Sequence of a Street Scene,” CGIP 20 199–228 (1982).Google Scholar
  2. 2.
    M. Haag and H.-H. Nagel, “Combination of Edge Element and Optical Flow Estimates for 3D-Model-Based Vehicle Tracking in Traffic Sequences,” Int. Journal of Computer Vision 35(3), 295–319 (1999).CrossRefGoogle Scholar
  3. 3.
    Eds. by S. Hinz, R. Bamler, and U. Stilla, “Airborne and Spaceborne Traffic Monitoring,” Theme Issue of ISPRS Journal of Photogrammetry and Remote Sensing 61(3/4) (2006).Google Scholar
  4. 4.
    S. Hinz and A. Baumgartner, “Automatic Extraction of Urban Road Nets from Multi-View Aerial Imagery,” ISPRS Journal of Photogrammetry and Remote Sensing 58(1–2), 83–98 (2003).CrossRefGoogle Scholar
  5. 5.
    J. Kang, I. Cohen, G. Medioni, and C. Yuan, “Detection and Tracking of Moving Objects from a Moving Platform in Presence of Strong Parallax,” International Conference on Computer Vision, 2005, vol. I, pp. 10–17.Google Scholar
  6. 6.
    D. Lenhart and S. Hinz, “Automatic Vehicle Tracking in Low Frame Rate Aerial Image Sequences,” Ed. by W. Forstner and R. Steffen, PCV06. IAPRS 36(3), 203–208 (2006).Google Scholar
  7. 7.
    C. Steger, “Similarity Measures for Occlusion, Clutter, and Illumination Invariant Object Recognition,” Ed. by B. Radig and S. Florczyk, Pattern Recognition, DAGM 2001, LNCS 2191, Springer Verlag, 148–154 (2001).Google Scholar
  8. 8.
    M. Ulrich, “Hierarchical Real-Time Recognition of Compound Objects in Images,” Dissertation, German Geodetic Commission (DGK), vol. C (2003).Google Scholar
  9. 9.
    Q. Yu, I. Cohen, G. Medioni, and B. Wu, “Boosted Markov Chain Monte Carlo Data Association for Multiple Target Detection and Tracking,” International Conference on Pattern Recognition 2006 (2006), vol. II, pp. 675–678.Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2008

Authors and Affiliations

  1. 1.Remote Sensing TechnologyTechnische Universität MünchenMunichGermany
  2. 2.Photogrammetry and Remote SensingTechnische Universität MünchenMunichGermany

Personalised recommendations