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Sensors 2012, 12(11), 15376-15393; doi:10.3390/s121115376
Article

GrabCut-Based Human Segmentation in Video Sequences

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Received: 4 September 2012; in revised form: 1 November 2012 / Accepted: 6 November 2012 / Published: 9 November 2012
(This article belongs to the Section Physical Sensors)
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Abstract: 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.
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 which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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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.

AMA Style

Hernández-Vela A, Reyes M, Ponce V, Escalera S. GrabCut-Based Human Segmentation in Video Sequences. Sensors. 2012; 12(11):15376-15393.

Chicago/Turabian Style

Hernández-Vela, Antonio; Reyes, Miguel; Ponce, Víctor; Escalera, Sergio. 2012. "GrabCut-Based Human Segmentation in Video Sequences." Sensors 12, no. 11: 15376-15393.



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