Name
Title | ||
|---|---|---|
Classifying children with 3D depth cameras for enabling children's safety applications |
Abstract | ||
|---|---|---|
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 ChildSafe 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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2632048.2636074 | UbiComp |
Keywords | Field | DocType |
child classification,design methodology,kinect-based applications | Histogram,Computer vision,False positive rate,Pattern recognition,Child protection,Computer science,Ranging,Human–computer interaction,Artificial intelligence,Classifier (linguistics),Classification rate | Conference |
Citations | PageRank | References |
2 | 0.46 | 9 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Can Başaran | 1 | 87 | 7.72 |
| Hee-jung Yoon | 2 | 8 | 5.64 |
| Ho Kyung Ra | 3 | 2 | 0.46 |
| Sang Hyuk Son | 4 | 1327 | 348.44 |
| Tae-joon Park | 5 | 349 | 37.39 |
| JeongGil Ko | 6 | 4 | 4.91 |