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