Enhancing People Counting in Cluttered Environments Using mm-Wave Radar, LSTM, and Ensemble Learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0197926" target="_blank" >RIV/00216305:26220/26:0197926 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/abstract/document/11077632" target="_blank" >https://ieeexplore.ieee.org/abstract/document/11077632</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/WFCS63373.2025.11077632" target="_blank" >10.1109/WFCS63373.2025.11077632</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Enhancing People Counting in Cluttered Environments Using mm-Wave Radar, LSTM, and Ensemble Learning

Popis výsledku v původním jazyce

Radar-based sensing in cluttered environments to ensure safety, and optimize resource allocation is a challenging task. Issues such as unwanted reflections, target signal blocking, and the need to collect new datasets for each new environment make it an effort-intensive process. To tackle these challenges, we’ve developed a radar-based system for counting people, making use of our previously developed Orthogonal Time Frequency Space joint communication and sensing system. To make sure that the received radar signals are as clear and as useful as they can, we performed several required preprocessing steps, such as static clutter removal or wavelet thresholding. We employed both CNN and LSTM networks to capture the spatial and temporal patterns in the training data from two different environments. To make the system more reliable and to make it able to generalize to new, unseen industrial settings, we incorporated an ensemble learning method with a voting mechanism. The effectiveness of this approach is promising in that it achieved 85% accuracy in counting up to 3 people in dynamic and cluttered environments. This shows its potential to effectively deal with the key characteristics of real-world people counting tasks.

Název v anglickém jazyce

Enhancing People Counting in Cluttered Environments Using mm-Wave Radar, LSTM, and Ensemble Learning

Popis výsledku anglicky

Radar-based sensing in cluttered environments to ensure safety, and optimize resource allocation is a challenging task. Issues such as unwanted reflections, target signal blocking, and the need to collect new datasets for each new environment make it an effort-intensive process. To tackle these challenges, we’ve developed a radar-based system for counting people, making use of our previously developed Orthogonal Time Frequency Space joint communication and sensing system. To make sure that the received radar signals are as clear and as useful as they can, we performed several required preprocessing steps, such as static clutter removal or wavelet thresholding. We employed both CNN and LSTM networks to capture the spatial and temporal patterns in the training data from two different environments. To make the system more reliable and to make it able to generalize to new, unseen industrial settings, we incorporated an ensemble learning method with a voting mechanism. The effectiveness of this approach is promising in that it achieved 85% accuracy in counting up to 3 people in dynamic and cluttered environments. This shows its potential to effectively deal with the key characteristics of real-world people counting tasks.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20202 - Communication engineering and systems

Projekt

<a href="/cs/project/LUC24141" target="_blank" >LUC24141: Simultánní komunikace a snímání pro zvyšování robustnosti systémů 6G</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

proceedings of IEEE 21st International Conference on Factory Communication Systems (WFCS)

ISBN

979-8-3315-3006-8

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

—

Název nakladatele

IEEE

Místo vydání

Rostock, Germany

Místo konání akce

Rostock

Datum konání akce

10. 6. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001556391900052