Paper Info
Title
Multiview foreground segmentation using 3D probabilistic model
Abstract
We propose a complete multi-view foreground segmentation and 3D reconstruction system that defines a 3-dimensional probabilistic model to model the foreground object in the 3 spatial dimensions, thus gathering the information from all the camera views. This 3D model is projected to each one of the views in order to perform the 2D segmentation with the foreground information shared by all the cameras. Then, for each one of the views, a MAP-MRF classification framework is applied between the projected region-based foreground model, the pixel-wise background model and the region-based shadow model defined for each view. The resultant masks are used to compute the next 3-dimensional reconstruction. This system achieves correct results by reducing the false positive and false negative errors in sequences where some camera sensors can present camouflage situations between foreground and background. Moreover, the use of the 3D model opens possibilities to use it for objects recognition or human activity understanding.
Year
DOI
Venue
2014
10.1109/ICIP.2014.7025671
Image Processing
Keywords
DocType
ISSN
image classification,image segmentation,maximum likelihood estimation,solid modelling,2D segmentation,3D probabilistic model,3D reconstruction system,MAP-MRF classification framework,false negative error reduction,false positive error reduction,foreground object,human activity understanding,multiview foreground segmentation,object recognition,pixel-wise background model,region-based foreground model,region-based shadow model,three-dimensional probabilistic model,3D probabilistic model,3D reconstruction,Multi-view foreground segmentation,SCGMM
Conference
1522-4880
Citations 
PageRank 
References 
0
0.34
12
Authors
2
Name
Order
Citations
PageRank
Jaime Gallego1564.90
Montse Pardàs234335.03