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GrabCut-Based Human Segmentation in Video Sequences

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. Green open access

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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
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URI: https://discovery.ucl.ac.uk/id/eprint/10114945
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