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arXiv · 2110.14491

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

Marco Ferri·Dario Mantegazza·Elia Cereda·Nicky Zimmerman·Luca M. Gambardella·Daniele Palossi·Jérôme Guzzi·Alessandro Giusti
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Abstract

We consider the task of visually estimating the pose of a human from images acquired by a nearby nano-drone; in this context, we propose a data augmentation approach based on synthetic background substitution to learn a lightweight CNN model from a small real-world training set. Experimental results on data from two different labs proves that the approach improves generalization to unseen environments.

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Marco Ferri, Dario Mantegazza, Elia Cereda, Nicky Zimmerman, Luca M. Gambardella, Daniele Palossi, Jérôme Guzzi, Alessandro Giusti. 2021-10-27. Training Lightweight CNNs for Human-Nanodrone Proximity Interaction from Small Datasets using Background Randomization. https://arxiv.org/abs/2110.14491

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