Real-time tracking of multiple occluding objects using level sets

We derive a probabilistic framework for robust, realtime, visual tracking of multiple previously unseen objects from a moving camera. This framework models the discrete depth ordering of the objects being tracked in the scene. The method uses the observed image data to compute a posterior over the o...

Full description

Saved in:
Bibliographic Details
Published in2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition pp. 1307 - 1314
Main Authors Bibby, C, Reid, I
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2010
Subjects
Application software
Cameras
Filtering
Layout
Level set
Particle tracking
Resilience
Robustness
Shape
Video sequences
Online AccessGet full text

Cover

More Information
Summary:We derive a probabilistic framework for robust, realtime, visual tracking of multiple previously unseen objects from a moving camera. This framework models the discrete depth ordering of the objects being tracked in the scene. The method uses the observed image data to compute a posterior over the objects' poses, shapes and relative depths. The poses are group transformations, the shapes are implicit contours represented using level-sets and the relative depths give the discrete depth ordering of the objects. All nuisance variables are marginalised out at the pixel-level resulting in a pixel-wise posterior, as opposed to a pixel-wise likelihood, and we show using quantitative results that this provides increased resilience to noise. We also demonstrate how motion models can be incorporated within the same probabilistic framework and show how this enables the system to track complete occlusions. The effectiveness of our method is demonstrated on a variety of challenging video sequences.
ISBN:1424469848
9781424469840
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2010.5539818