PT Journal
AU Antonio Hernandez
   Miguel Reyes
   Victor Ponce
   Sergio Escalera
TI GrabCut-Based Human Segmentation in Video Sequences
SO Sensors
JI SENS
PY 2012
BP 15376
EP 15393
VL 12
IS 11
DI 10.3390/s121115376
DE segmentation; human pose recovery; GrabCut; GraphCut; Active Appearance Models; Conditional Random Field
AB 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.
ER