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Classifying children with 3D depth cameras for enabling children's safety applications

Title
Classifying children with 3D depth cameras for enabling children's safety applications
Authors
Basaran, CanYoon, Hee JungRa, Ho KyungSon, Sang HyukPark, TaejoonKo, JeongGile
DGIST Authors
Basaran, Can
Issue Date
2014
Citation
2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2014, 343-347
Type
Conference
Article Type
Conference Paper
ISBN
9780000000000
Abstract
In this work, we present ChildSafe, a classification sys- Tem which exploits human skeletal features collected us- ing a 3D depth camera to classify visual characteristics between children and adults. ChildSafe analyzes the histograms of training samples and implements a bin- boundary-based classifier. We train and evaluate Child- Safe using a large dataset of visual samples collected from 150 elementary school children and 43 adults, rang- ing in the ages of 7 and 50. Our results suggest that ChildSafe successfully detects children with a proper classification rate of up to 97%, a false negative rate of as low as 1.82%, and a low false positive rate of 1.46%. We envision this work as an effective sub-system for de- signing various child protection applications. Copyright © 2014 by the Association for Computing Machinery, Inc. (ACM).
URI
http://hdl.handle.net/20.500.11750/3783
DOI
10.1145/2632048.2636074
Publisher
Association for Computing Machinery
Files:
There are no files associated with this item.
Collection:
ETC2. Conference Papers


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