Real-time Distance Measurement in a 2D Image on Hardware with Limited Resources for Low-power IoT Devices (Radar Control System)

Jurij Kuzmic, Günter Rudolph

2022

Abstract

This paper presents an approach for real-time distance measurement in a 2D image on hardware with limited resources without a reference object. Additionally, different approximated functions for distance measurement are presented. Here, we focus on an approach to develop real-time distance detection for hardware with limited resources in the field of the Internet of Things (IoT). Also, our distance measurement system is evaluated with simulated data, real data from model making area and data from a real vehicle from real environment. In the beginning, related work of this paper is discussed. The data acquisition of the different simulated and real data sets is also discussed in this paper. Additionally, dissimilar resolutions for distance measurement are compared in accuracy and run time to find the better and faster system for distance measurement in a 2D image on hardware with limited resources for low-power IoT devices. Through the experiments described in this paper, the comparison of the run time depending on different IoT hardware is presented. Here, the idea is to develop a radar control system for self-driving cars from model making area and vehicles from real environment. Finally, future research and work in this area are discussed.

Paper Citation

in Harvard Style

Kuzmic J. and Rudolph G. (2022). Real-time Distance Measurement in a 2D Image on Hardware with Limited Resources for Low-power IoT Devices (Radar Control System). In Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA, ISBN 978-989-758-584-5, pages 94-101. DOI: 10.5220/0011188100003277

in Bibtex Style

@conference{delta22,
author={Jurij Kuzmic and Günter Rudolph},
title={Real-time Distance Measurement in a 2D Image on Hardware with Limited Resources for Low-power IoT Devices (Radar Control System)},
booktitle={Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,},
year={2022},
pages={94-101},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011188100003277},
isbn={978-989-758-584-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,
TI - Real-time Distance Measurement in a 2D Image on Hardware with Limited Resources for Low-power IoT Devices (Radar Control System)
SN - 978-989-758-584-5
AU - Kuzmic J.
AU - Rudolph G.
PY - 2022
SP - 94
EP - 101
DO - 10.5220/0011188100003277