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3D Object Trajectory Reconstruction using Stereo Matching and Instance Flow based Multiple Object Tracking

 
: Bullinger, Sebastian; Bodensteiner, Christoph; Arens, Michael

:
Volltext urn:nbn:de:0011-n-5462845 (8.5 MByte PDF)
MD5 Fingerprint: 10acd5fc7d54a3d622c76d79b6c9ebfd
Erstellt am: 6.6.2019


MVA Organization:
MVA 2019, International Conference on Machine Vision Applications. Online resource : May 27-31, 2019 National Olympics Memorial Youth Center, Tokyo, Japan
Online im WWW, 2019
http://www.mva-org.jp/Proceedings/2019/
6 S.
International Conference on Machine Vision Applications (MVA) <16, 2019, Tokyo>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

Abstract
This paper presents a method to reconstruct three-dimensional object motion trajectories in stereo video sequences. We apply stereo matching to each image pair of a stereo sequence to compute corresponding binocular disparities. By combining instance-aware semantic segmentation techniques and optical
flow cues, we track two-dimensional object shapes on pixel level. This allows us to determine for each frame pair object specifc disparities and corresponding object points. By applying Structure from Motion (SfM) we compute camera poses with respect to background structures. We embed the
vehicle trajectories into the environment reconstruction by combining the object point cloud of each image pair with corresponding camera poses contained in the background SfM reconstruction. We show qualitative results
on the KITTI and CityScapes dataset and compare our method quantitatively with previously published monocular approaches on synthetic data of vehicles in an urban environment. We achieve an average trajectory error of 0:11 meter.

: http://publica.fraunhofer.de/dokumente/N-546284.html