Age Group Classifier of Adults and Children with YOLO-based Deep Learning Pre-Processing Scheme for Embedded Platforms

Based on the information of body proportion, in this study, a simple and effective processing scheme is developed for two age groups classification, i.e. children and adults for the applications of smart autonomous movers. By the YOLO-based CNN model for head and body objects detections, the recognition accuracies of age group classification for children and adults are 95% and 92.5% respectively with the image datasets collected in publics. Compared with the existed design, the proposed methodology performs simpler and more effective recognition capability for age group classification of adults and children. The proposed design is implemented on GPU-based embedded platform for real-time applications.