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Real-time semantic clothing segmentation

Real-time semantic clothing segmentation
Real-time semantic clothing segmentation
Clothing segmentation is a challenging field of research which is rapidly gaining attention. This paper presents a system for semantic segmentation of primarily monochromatic clothing and printed/stitched textures in single images or live video. This is especially appealing to emerging augmented reality applications such as retexturing sports players' shirts with localized adverts or statistics in TV/internet broadcasting. We initialise points on the upper body clothing by body fiducials rather than by applying distance metrics to a detected face. This helps prevent segmentation of the skin rather than clothing. We take advantage of hue and intensity histograms incorporating spatial priors to develop an efficient segmentation method. Evaluated against ground truth on a dataset of 100 people, mostly in groups, the accuracy has an average F-score of 0.97 with an approach which can be over 88% more efficient than the state of the art.
Cushen, George
52f73d41-3ae0-4c11-a50a-86e782c03745
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Cushen, George
52f73d41-3ae0-4c11-a50a-86e782c03745
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12

Cushen, George and Nixon, Mark S. (2012) Real-time semantic clothing segmentation. International Symposium on Visual Computing, Greece.

Record type: Conference or Workshop Item (Paper)

Abstract

Clothing segmentation is a challenging field of research which is rapidly gaining attention. This paper presents a system for semantic segmentation of primarily monochromatic clothing and printed/stitched textures in single images or live video. This is especially appealing to emerging augmented reality applications such as retexturing sports players' shirts with localized adverts or statistics in TV/internet broadcasting. We initialise points on the upper body clothing by body fiducials rather than by applying distance metrics to a detected face. This helps prevent segmentation of the skin rather than clothing. We take advantage of hue and intensity histograms incorporating spatial priors to develop an efficient segmentation method. Evaluated against ground truth on a dataset of 100 people, mostly in groups, the accuracy has an average F-score of 0.97 with an approach which can be over 88% more efficient than the state of the art.

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More information

Published date: July 2012
Venue - Dates: International Symposium on Visual Computing, Greece, 2012-07-01
Related URLs:
Organisations: Vision, Learning and Control

Identifiers

Local EPrints ID: 340202
URI: https://eprints.soton.ac.uk/id/eprint/340202
PURE UUID: 8973db55-f5cf-4f24-b0b6-0028848c2c14
ORCID for Mark S. Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 14 Jun 2012 12:47
Last modified: 15 Aug 2019 00:58

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Contributors

Author: George Cushen
Author: Mark S. Nixon ORCID iD

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