Abstract

This paper discusses the detection of moving objects (being a crucial part of driver assistance systems) using monocular or stereoscopic computer vision. In both cases, object detection is based on motion analysis of individually tracked image points (optical flow), providing a motion metric which corresponds to the likelihood that the tracked point is moving. Based on this metric, points are segmented into objects by employing a globally optimal graph-cut algorithm. Both approaches are comparatively evaluated using real-world vehicle image sequences.


Original document

The different versions of the original document can be found in:

http://dx.doi.org/10.1007/978-3-540-92957-4_53 under the license http://www.springer.com/tdm
https://researchspace.auckland.ac.nz/handle/2292/3263,
https://dblp.uni-trier.de/db/conf/psivt/psivt2009.html#KlappsteinVRWK09,
https://link.springer.com/content/pdf/10.1007%2F978-3-540-92957-4_53.pdf,
https://www.scipedia.com/public/Klappstein_et_al_2009a,
http://dx.doi.org/10.1007/978-3-540-92957-4_53,
https://doi.org/10.1007/978-3-540-92957-4_53,
https://researchspace.auckland.ac.nz/bitstream/2292/3263/2/MItech-TR-22.pdf,
https://academic.microsoft.com/#/detail/1690959182
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Document information

Published on 01/01/2009

Volume 2009, 2009
DOI: 10.1007/978-3-540-92957-4_53
Licence: CC BY-NC-SA license

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