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Because of the COVID-19 pandemic, gaze tracking for nontouch user interface designs used in advertising displays or automatic vending machines has become an emerging research topic. In this study, a cost-effective deep-learning-based customer gaze direction detection technology was developed for a smart advertising display. To achieve calibration-free interactions between customers and displays, the You-Only-Look-Once (YOLO)-v3-tiny-based deep learning model was used for determining the bounding boxes of eyes and pupils. Next, postprocessing was conducted using a voting mechanism and difference vectors between the central coordinates of the bounding boxes for effectively predicting customer gaze directions. Product images were separated into two or four gaze zones. For cross-person testing, the Recall, Precision, Accuracy, and F1-score for two gaze zones were approximately 77%, 99%, 88%, and 87%, respectively, and those for four gaze zones were approximately 72%, 91%, 91%, and 79%, respectively. Software implementations on NVIDIA graphics-processing-unit-accelerated embedded platforms exhibited a frame rate of nearly 30 frames per second. The proposed design achieved real-time gaze direction detection for a smart advertising platform.

Calibration-free and deep-learning-based customer gaze direction detection technology based on the YOLOv3-tiny model for smart advertising displays