Background modeling: dealing with pan, tilt or zoom in videos
Date issued
2017
Journal Title
Journal ISSN
Volume Title
Publisher
Václav Skala - UNION Agency
Abstract
Even simple camera movements like pan, tilt or zoom constitute enormous problems for background subtraction
algorithms since the modeling of the background works only under the assumption of a static camera. The problem
has been mostly ignored and other algorithms have been used for videos with non-static cameras. Nonetheless, in
this paper we introduce a method that adapts the background model to these camera movements by using affine
transformations in combination with a similarity metric, and thereby the algorithm makes background subtraction
usable for these situations. Also, to keep the generality of this approach, we first apply a detection step to avoid
unnecessary adaptions in videos with a static camera because even small adaptions might otherwise deteriorate
the background model over time. The method is evaluated on the extensive changedetection.net data set and
could reliably detect camera motion in all videos as well as precisely adapt the model of the background to that
motion. This does improve the quality of the background models significantly which consequently leads to a higher
accuracy of the segmentations.
Description
Subject(s)
odebírání pozadí, modelování pozadí, segmentace videa, detekce změny, sklon, zoom
Citation
WSCG 2017: poster papers proceedings: 25th International Conference in Central Europe on Computer Graphics, Visualization and Computer Visionin co-operation with EUROGRAPHICS Association, p. 53-58.