Fuzzy bin-based classification for detecting children's presence with 3D depth cameras

Hee Jung Yoon, Ho Kyeong Ra, Can Basaran, Sang Hyuk Son, Taejoon Park, Jeonggil Ko

Research output: Contribution to journalArticle

1 Citation (Scopus)

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 lowas 1.82%, and a lowfalse-positive rate of 5.14%.We envision this work as a first step, an effective subsystem for designing child safety applications.

Original languageEnglish
Article number21
JournalACM Transactions on Sensor Networks
Volume13
Issue number3
DOIs
Publication statusPublished - 2017 Aug

Fingerprint

Bins
Cameras
Fuzzy sets
Engines

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

Cite this

Yoon, Hee Jung ; Ra, Ho Kyeong ; Basaran, Can ; Son, Sang Hyuk ; Park, Taejoon ; Ko, Jeonggil. / Fuzzy bin-based classification for detecting children's presence with 3D depth cameras. In: ACM Transactions on Sensor Networks. 2017 ; Vol. 13, No. 3.
@article{fe5b9582a1bd4221ac3b55ae9d039b23,
title = "Fuzzy bin-based classification for detecting children's presence with 3D depth cameras",
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 lowas 1.82{\%}, and a lowfalse-positive rate of 5.14{\%}.We envision this work as a first step, an effective subsystem for designing child safety applications.",
author = "Yoon, {Hee Jung} and Ra, {Ho Kyeong} and Can Basaran and Son, {Sang Hyuk} and Taejoon Park and Jeonggil Ko",
year = "2017",
month = "8",
doi = "10.1145/3079764",
language = "English",
volume = "13",
journal = "ACM Transactions on Sensor Networks",
issn = "1550-4859",
publisher = "Association for Computing Machinery (ACM)",
number = "3",

}

Fuzzy bin-based classification for detecting children's presence with 3D depth cameras. / Yoon, Hee Jung; Ra, Ho Kyeong; Basaran, Can; Son, Sang Hyuk; Park, Taejoon; Ko, Jeonggil.

In: ACM Transactions on Sensor Networks, Vol. 13, No. 3, 21, 08.2017.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Fuzzy bin-based classification for detecting children's presence with 3D depth cameras

AU - Yoon, Hee Jung

AU - Ra, Ho Kyeong

AU - Basaran, Can

AU - Son, Sang Hyuk

AU - Park, Taejoon

AU - Ko, Jeonggil

PY - 2017/8

Y1 - 2017/8

N2 - 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 lowas 1.82%, and a lowfalse-positive rate of 5.14%.We envision this work as a first step, an effective subsystem for designing child safety applications.

AB - 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 lowas 1.82%, and a lowfalse-positive rate of 5.14%.We envision this work as a first step, an effective subsystem for designing child safety applications.

UR - http://www.scopus.com/inward/record.url?scp=85028539467&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85028539467&partnerID=8YFLogxK

U2 - 10.1145/3079764

DO - 10.1145/3079764

M3 - Article

AN - SCOPUS:85028539467

VL - 13

JO - ACM Transactions on Sensor Networks

JF - ACM Transactions on Sensor Networks

SN - 1550-4859

IS - 3

M1 - 21

ER -