Moving Object Segmentation Using Optical Flow and Depth Information

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:

• https://link.springer.com/content/pdf/10.1007%2F978-3-540-92957-4_53.pdf

• http://link.springer.com/content/pdf/10.1007/978-3-540-92957-4_53,

http://dx.doi.org/10.1007/978-3-540-92957-4_53 under the license http://www.springer.com/tdm

• https://link.springer.com/chapter/10.1007/978-3-540-92957-4_53,

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