Classifying Children with 3D Depth Cameras for Enabling Children's Safety Applications

Date

2014-09-17

Author

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

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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In this work, we present ChildSafe, a classification system which exploits human skeletal features collected using 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, ranging 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 designing various child protection applications.

Subject Keywords

Child classification, Kinect-based applications

URI

https://hdl.handle.net/11511/68294

DOI

https://doi.org/10.1145/2632048.2636074

Conference Name

ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp)

Collections

Engineering, Conference / Seminar

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Citation Formats

C. Basaran, H. J. Yoon, H. K. Ra, S. H. Son, T. Park, and J. Ko, “Classifying Children with 3D Depth Cameras for Enabling Children’s Safety Applications,” Seattle, WA, 2014, p. 343, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/68294.