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

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3 Citations (Scopus)


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
Issue number3
Publication statusPublished - 2017 Aug

Bibliographical note

Funding Information:
This work was partially supported by the Ministry of Trade, Industry and Energy (Grant #N0002312) as part of the Industrial Infrastructure Program for Fundamental Technologies, DGIST Research and Development Program (CPS Global Center) funded by the Ministry of Science, ICT & Future Planning for the project “Identifying Unmet Requirements for Future Wearable Devices in Designing Autonomous Clinical Event Detection Applications”, and Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korean government (MSIP) (No. B0101-15-0557, Resilient Cyber-Physical Systems Research). Authors’ addresses: H. J. Yoon, H.-K. Ra, and S. H. Son, Information & Communication Engineering Department, DGIST, 333 Techno Jungang-daero, Hyeongpung-myeon, Dalseong-gun, Daegu, Republic of Korea, 42988; emails: {heejung8, hk, son}@dgist.ac.kr; C. Basaran, HERE, Invalidenstraße 116, Mitte, 10115, Berlin, Germany; email: basaran.can@gmail.com; T. Park, Robotics Engineering Department, Hanyang University, Hanyangdaehak-ro, Sangnok-gu, Ansan-si, Gyeonggi-do, Republic of Korea, 15588; email: taejoon@hanyang.ac.kr; J. Ko, Software and Computer Engineering Department, Ajou University, 206 Worldcup-Ro, Yeongtong-Gu, Suwon, Gyunggi-Do, Republic of Korea, 16499; email: jgko@ajou.ac.kr. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax + 1 (212) 869-0481, or permissions@acm.org. © 2017 ACM 1550-4859/2017/08-ART21 $15.00 https://doi.org/10.1145/3079764

Publisher Copyright:
© 2017 ACM.

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications


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