%0 Journal Article
%T GrabCut-Based Human Segmentation in Video Sequences
%A Antonio Hernandez
%A Miguel Reyes
%A Victor Ponce
%A Sergio Escalera
%J Sensors
%D 2012
%V 12
%N 11
%F Antonio Hernandez2012
%O HuPBA;MILAB
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2147), last updated on Thu, 13 Mar 2014 16:10:05 +0100
%X In this paper, we present a fully-automatic Spatio-Temporal GrabCut human segmentation methodology that combines tracking and segmentation. GrabCut initialization is performed by a HOG-based subject detection, face detection, and skin color model. Spatial information is included by Mean Shift clustering whereas temporal coherence is considered by the historical of Gaussian Mixture Models. Moreover, full face and pose recovery is obtained by combining human segmentation with Active Appearance Models and Conditional Random Fields. Results over public datasets and in a new Human Limb dataset show a robust segmentation and recovery of both face and pose using the presented methodology.
%K segmentation
%K human pose recovery
%K GrabCut
%K GraphCut
%K Active Appearance Models
%K Conditional Random Field
%U http://refbase.cvc.uab.es/files/HRP2012.pdf
%U http://dx.doi.org/10.3390/s121115376
%P 15376-15393