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 journalArticlepeer-review

5 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

Publisher Copyright:
© 2017 ACM.

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications


Dive into the research topics of 'Fuzzy bin-based classification for detecting children's presence with 3D depth cameras'. Together they form a unique fingerprint.

Cite this