Realtime depth estimation and obstacle detection from monocular video

Andreas Wedel, Uwe Franke, Jens Klappstein, Thomas Brox, Daniel Cremers

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

31 Scopus citations


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.

Original languageEnglish
Title of host publicationPattern Recognition - 28th DAGM Symposium, Proceedings
PublisherSpringer Verlag
Number of pages10
ISBN (Print)3540444122, 9783540444121
StatePublished - 2006
Externally publishedYes
Event28th Symposium of the German Association for Pattern Recognition, DAGM 2006 - Berlin, Germany
Duration: 12 Sep 200614 Sep 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4174 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference28th Symposium of the German Association for Pattern Recognition, DAGM 2006


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