Training Lightweight CNNs for Human-Nanodrone Proximity Interaction from Small Datasets using Background Randomization
@article{Ferri2021TrainingLC, title={Training Lightweight CNNs for Human-Nanodrone Proximity Interaction from Small Datasets using Background Randomization}, author={Marco Ferri and Dario Mantegazza and Elia Cereda and Nicky Zimmerman and Luca Maria Gambardella and Daniele Palossi and J{\'e}r{\^o}me Guzzi and Alessandro Giusti}, journal={ArXiv}, year={2021}, volume={abs/2110.14491} }
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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