Hernández-Vela, A;
Reyes, M;
Ponce, V;
Escalera, S;
(2012)
GrabCut-Based Human Segmentation in Video Sequences.
Sensors
, 12
(11)
pp. 15376-15393.
10.3390/s121115376.
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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.
| Type: | Article |
|---|---|
| Title: | GrabCut-Based Human Segmentation in Video Sequences |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3390/s121115376 |
| Publisher version: | https://doi.org/10.3390/s121115376 |
| Language: | English |
| Additional information: | This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by-nc-sa/3.0/). |
| Keywords: | segmentation; human pose recovery; GrabCut; GraphCut; Active Appearance Models; Conditional Random Field |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10114945 |
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