research-article

Fuzzy Bin-Based Classification for Detecting Children’s Presence with 3D Depth Cameras

Authors:

Jeonggil KoAuthors Info & Claims

ACM Transactions on Sensor Networks (TOSN), Volume 13, Issue 3

Article No.: 21, Pages 1 - 28

https://doi.org/10.1145/3079764

Published: 16 August 2017 Publication History

Abstract

With the advancement of technology in various domains, many efforts have been made to design advanced classification engines that aid the protection of civilians and their properties in different settings. In this work, we focus on a set of the population which is probably the most vulnerable: children. Specifically, we present ChildSafe, a classification system that exploits ratios of skeletal features extracted from children and adults using a 3D depth camera to classify visual characteristics between the two age groups. Specifically, we combine the ratio information into one bag-of-words feature for each sample, where each word is a histogram of the ratios. ChildSafe analyzes the words that are normalized within and between the two age groups and implements a fuzzy bin-based classification method that represents bin-boundaries using fuzzy sets. We train and evaluate ChildSafe using a large dataset of visual samples collected from 150 elementary school children and 150 adults, ranging in age from 7 to 50. Our results suggest that ChildSafe successfully detects children with a proper classification rate of up to 94%, a false-negative rate as low as 1.82%, and a low false-positive rate of 5.14%. We envision this work as a first step, an effective subsystem for designing child safety applications.

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Cited By

Su YZhang FNiu KWang TJin BWang ZJiang YZhang DQiu LXiong J(2024)Embracing Distributed Acoustic Sensing in Car Cabin for Children Presence DetectionProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435488:1(1-28)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643548
Kumar PO'Connell FLi LByrne VChetty MClegg TVitak J(2023)Understanding Research Related to Designing for Children's Privacy and Security: A Document AnalysisProceedings of the 22nd Annual ACM Interaction Design and Children Conference10.1145/3585088.3589375(335-354)Online publication date: 19-Jun-2023
https://dl.acm.org/doi/10.1145/3585088.3589375
Arefin Nazira FGosh SNur KPoul Singh SMridha M(2022)Ensure Safe Internet for Children and Teenagers Using Deep Learning2022 International Conference on Decision Aid Sciences and Applications (DASA)10.1109/DASA54658.2022.9765035(395-400)Online publication date: 23-Mar-2022
https://doi.org/10.1109/DASA54658.2022.9765035
Show More Cited By

Index Terms

Fuzzy Bin-Based Classification for Detecting Children’s Presence with 3D Depth Cameras
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Biometrics
        Scene anomaly detection
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning

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Published In

cover image ACM Transactions on Sensor Networks

ACM Transactions on Sensor Networks Volume 13, Issue 3

August 2017

308 pages

ISSN:1550-4859

EISSN:1550-4867

DOI:10.1145/3129740

Editor:
Chenyang Lu
Department of Computer Science and Engineering / School of Engineering and Applied Sciences / Washington University / 1 Brookings Drive / St. Louis, MO 63130

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Copyright © 2017 ACM.

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Association for Computing Machinery

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 16 August 2017

Accepted: 01 April 2017

Revised: 01 April 2017

Received: 01 March 2016

Published in�TOSN�Volume 13, Issue 3

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DGIST Research and Development Program (CPS Global Center)
ICT 8 Future Planning for the project “Identifying Unmet Requirements for Future Wearable Devices in Designing Autonomous Clinical Event Detection Applications”
Ministry of Science
Ministry of Trade, Industry and Energy
Industrial Infrastructure Program for Fundamental Technologies
Institute for Information 8 Communications Technology Promotion (IITP) grant funded by the Korean government (MSIP)
Resilient Cyber-Physical Systems Research

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Cited By

Su YZhang FNiu KWang TJin BWang ZJiang YZhang DQiu LXiong J(2024)Embracing Distributed Acoustic Sensing in Car Cabin for Children Presence DetectionProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435488:1(1-28)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643548
Kumar PO'Connell FLi LByrne VChetty MClegg TVitak J(2023)Understanding Research Related to Designing for Children's Privacy and Security: A Document AnalysisProceedings of the 22nd Annual ACM Interaction Design and Children Conference10.1145/3585088.3589375(335-354)Online publication date: 19-Jun-2023
https://dl.acm.org/doi/10.1145/3585088.3589375
Arefin Nazira FGosh SNur KPoul Singh SMridha M(2022)Ensure Safe Internet for Children and Teenagers Using Deep Learning2022 International Conference on Decision Aid Sciences and Applications (DASA)10.1109/DASA54658.2022.9765035(395-400)Online publication date: 23-Mar-2022
https://doi.org/10.1109/DASA54658.2022.9765035
Kim HPark JChoung YJang JKo J(2021)Predicting speech discrimination scores from pure-tone thresholds—A machine learning-based approach using data from 12,697 subjectsPLOS ONE10.1371/journal.pone.026143316:12(e0261433)Online publication date: 31-Dec-2021
https://doi.org/10.1371/journal.pone.0261433

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