  Most Gaze estimation research only works on a setup condition that a camera
perfectly captures eyes gaze. They have not literarily specified how to set up
a camera correctly for a given position of a person. In this paper, we carry
out a study on gaze estimation with a logical camera setup position. We further
bring our research in a practical application by using inexpensive edge devices
with a realistic scenario. That is, we first set up a shopping environment
where we want to grasp customers gazing behaviors. This setup needs an optimal
camera position in order to maintain estimation accuracy from existing gaze
estimation research. We then apply the state-of-the-art of few-shot learning
gaze estimation to reduce training sampling in the inference phase. In the
experiment, we perform our implemented research on NVIDIA Jetson TX2 and
achieve a reasonable speed, 12 FPS which is faster compared with our reference
work, without much degradation of gaze estimation accuracy. The source code is
released at https://github.com/linh-gist/GazeEstimationTX2.

Computer Vision and Pattern Recognition;Artificial Intelligence