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We consider the task of visually estimating the pose of a human from imagesacquired by a nearby nano-drone; in this context, we propose a dataaugmentation approach based on synthetic background substitution to learn alightweight CNN model from a small real-world training set. Experimentalresults on data from two different labs proves that the approach improvesgeneralization to unseen environments.

Training Lightweight CNNs for Human-Nanodrone Proximity Interaction from Small Datasets using Background Randomization