Authors:
Martin Radolko
;
Fahimeh Farhadifard
and
Uwe von Lukas
Affiliation:
University Rostock and Fraunhofer Institute for Computer Fraphics Research IGD, Germany
Keyword(s):
Change Detection, Background Subtraction, Video Segmentation, Video Segregation, Underwater Segmentation, Flux Tensor.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Segmentation and Grouping
Abstract:
In this paper a new approach for change detection in videos of crowded scenes is proposed with the extended
Gaussian Switch Model in combination with a Flux Tensor pre-segmentation. The extended Gaussian Switch
Model enhances the previous method by combining it with the idea of the Mixture of Gaussian approach and
an intelligent update scheme which made it possible to create more accurate background models even for
difficult scenes. Furthermore, a foreground model was integrated and could deliver valuable information in
the segmentation process. To deal with very crowded areas in the scene – where the background is not visible
most of the time – we use the Flux Tensor to create a first coarse segmentation of the current frame and only
update areas that are almost motionless and therefore with high certainty should be classified as background.
To ensure the spatial coherence of the final segmentations, the N2Cut approach is added as a spatial model
after the background subtraction ste
p. The evaluation was done on an underwater change detection datasets
and showed significant improvements over previous methods, especially in the crowded scenes.
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