Realtime Depth Estimation and Obstacle Detection from Monocular Video

  • Andreas Wedel
  • Uwe Franke
  • Jens Klappstein
  • Thomas Brox
  • Daniel Cremers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4174)


This paper deals with the detection of arbitrary static objects in traffic scenes from monocular video using structure from motion. A camera in a moving vehicle observes the road course ahead. The camera translation in depth is known. Many structure from motion algorithms were proposed for detecting moving or nearby objects. However, detecting stationary distant obstacles in the focus of expansion remains quite challenging due to very small subpixel motion between frames. In this work the scene depth is estimated from the scaling of supervised image regions. We generate obstacle hypotheses from these depth estimates in image space. A second step then performs testing of these by comparing with the counter hypothesis of a free driveway. The approach can detect obstacles already at distances of 50m and more with a standard focal length. This early detection allows driver warning and safety precaution in good time.


Image Region Image Space Depth Estimation Large Time Scale Obstacle 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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Andreas Wedel
    • 1
    • 2
  • Uwe Franke
    • 1
  • Jens Klappstein
    • 1
  • Thomas Brox
    • 2
  • Daniel Cremers
    • 2
  1. 1.DaimlerChrysler Research and Technology, REI/AISindelfingenGermany
  2. 2.Computer Vision and Pattern Recognition GroupRheinische Friedrich-Wilhelms UniveristätBonnGermany

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