PORTO @ 
Archivio Istituzionale della Ricerca
IRIS è la soluzione IT che facilita la raccolta e la gestione dei dati relativi alle attività e ai prodotti della ricerca. Fornisce a ricercatori, amministratori e valutatori gli strumenti per monitorare i risultati della ricerca, aumentarne la visibilità e allocare in modo efficace le risorse disponibili.
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.
| Titolo: | A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge | |
| Autori: | ||
| Data di pubblicazione: | 2020 | |
| Rivista: | ||
| 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
Copyright © 2021