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

Can Basaran, Hee Jung Yoon, Ho Kyung Ra, Sang Hyuk Son, Taejoon Park, Jeong Gil Ko

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationUbiComp 2014 - Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing
PublisherAssociation for Computing Machinery, Inc
Pages343-347
Number of pages5
ISBN (Electronic)9781450329682
DOIs
Publication statusPublished - 2014
Event2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2014 - Seattle, United States
Duration: 2014 Sep 132014 Sep 17

Publication series

NameUbiComp 2014 - Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Other

Other2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2014
Country/TerritoryUnited States
CitySeattle
Period14/9/1314/9/17

Bibliographical note

Publisher Copyright:
Copyright © 2014 by the Association for Computing Machinery, Inc. (ACM).

All Science Journal Classification (ASJC) codes

  • Software

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