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EnglishThe vital statistics of the last century highlight a sharp increment of the average age of the world population with a consequent growth of the number of older people. Service robotics applications have the potentiality to provide systems and tools to support the autonomous and self-sufficient older adults in their houses in everyday life, thereby avoiding the task of monitoring them with third parties. In this context, we propose a cost-effective modular solution to detect and follow a person in an indoor, domestic environment. We exploited the latest advancements in deep learning optimization techniques, and we compared different neural network accelerators to provide a robust and flexible person-following system at the edge. Our proposed cost-effective and power-efficient solution is fully-integrable with pre-existing navigation stacks and creates the foundations for the development of fully-autonomous and self-contained service robotics applications.
A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge / Boschi, Anna; Salvetti, Francesco; Mazzia, Vittorio; Chiaberge, Marcello. - In: MACHINES. - ISSN 2075-1702. - ELETTRONICO. - 8:3(2020), pp. 49-67. [10.3390/machines8030049]
| Titolo: | A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge | |
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| Data di pubblicazione: | 2020 | |
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| Digital Object Identifier (DOI): | http://dx.doi.org/10.3390/machines8030049 | |
| Appare nelle tipologie: | 1.1 Articolo in rivista |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| jnl_2020_machines_personfollowing.pdf | Articolo principale | 2a Post-print versione editoriale / Version of Record |  | Visibile a tuttiVisualizza/Apri |
http://hdl.handle.net/11583/2843394
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