# A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge

arXiv: https://arxiv.org/abs/2008.13474 · HTML: https://mlsanity.com/paper/2008.13474/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2020.

## Built on by (most cited first, 2 of 2)

- [A Deep Learning Driven Algorithmic Pipeline for Autonomous Navigation in Row-Based Crops](https://mlsanity.com/paper/2112.03816/) (2021)
- [RL-DWA Omnidirectional Motion Planning for Person Following in Domestic Assistance and Monitoring](https://mlsanity.com/paper/2211.04993/) (2022)

## Abstract (arXiv)

The 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.