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Classifying children with 3D depth cameras for enabling children's safety applications
- Classifying children with 3D depth cameras for enabling children's safety applications
- Basaran, Can; Yoon, Hee Jung; Ra, Ho Kyung; Son, Sang Hyuk; Park, Taejoon; Ko, JeongGile
- DGIST Authors
- Basaran, Can
- Issue Date
- 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2014, 343-347
- Article Type
- Conference Paper
- 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).
- Association for Computing Machinery
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