Don't "Weight" to Board: Augmenting Vision-based PassengerWeight Prediction via Viscoelastic Mat

Airlines overestimate the weight of their passengers by simply assigning a constant weight for everyone, causing each plane to burn more fuel than needed to carry the extra weight. Accurately estimating the passenger weights is a difficult problem for airlines as naively weighing all passengers with scales is impractical in already busy airports. Hence, we propose CamScale, a novel vision-based weight inference system that is augmented by an off-the-shelf viscoelastic mat (e.g., memory foam mat). CamScale takes the video feed of the mat placed on the floor as the passengers walk over it. It utilizes the inherent strain, or deformation of the mat due to the passengers' footsteps to infer their weights. CamScale is advantageous because it does not incur additional weighing time, while being cost-effective and accurate. We evaluate CamScale through real-world experiments by deploying RGB and infrared cameras and inviting 36 participants to walk a total of more than 17,000 steps over viscoelastic mats, equivalent to walking approximately 13.1 km. We demonstrate that CamScale is able to accurately estimate an individual's weight with an average error of 1.12 kg.