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GrabCut-Based Human Segmentation in Video Sequences

Departamento MAIA, Universitat de Barcelona, Gran Via 585, 08007 Barcelona, Spain
Centre de Visió per Computador, Campus UAB, Edifici O, 08193 Bellaterra, Barcelona, Spain
Author to whom correspondence should be addressed.
Sensors 2012, 12(11), 15376-15393;
Received: 4 September 2012 / Revised: 1 November 2012 / Accepted: 6 November 2012 / Published: 9 November 2012
(This article belongs to the Section Physical Sensors)
PDF [1519 KB, uploaded 21 June 2014]


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. View Full-Text
Keywords: segmentation; human pose recovery; GrabCut; GraphCut; Active Appearance Models; Conditional Random Field segmentation; human pose recovery; GrabCut; GraphCut; Active Appearance Models; Conditional Random Field
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Hernández-Vela, A.; Reyes, M.; Ponce, V.; Escalera, S. GrabCut-Based Human Segmentation in Video Sequences. Sensors 2012, 12, 15376-15393.

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