Dynamic People Counting from Delay-Doppler Images in Challenging Scenarios: Enhancing Model Performance

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F24%3APU151167" target="_blank" >RIV/00216305:26220/24:PU151167 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10524098" target="_blank" >https://ieeexplore.ieee.org/document/10524098</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/RADIOELEKTRONIKA61599.2024.10524098" target="_blank" >10.1109/RADIOELEKTRONIKA61599.2024.10524098</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Dynamic People Counting from Delay-Doppler Images in Challenging Scenarios: Enhancing Model Performance

Popis výsledku v původním jazyce

This study presents a novel radar-based people counting (PCnt) methodology empowered by deep learning (DL) frameworks. The challenges we face include overfitting due to the model’s tendency to extract highly domain-specific features. These challenges arise from limited data and clutter in indoor settings. To tackle this, our radar system operates in both lab and industrial environments. We propose a 2D-CNN approach and explore ways to handle these challenges, focusing on im proving accuracy through preprocessing techniques. Additionally, we introduced data augmentation strategies to enhance model robustness and mitigate overfitting. Our experiments show our approach accurately counts people moving along the radar line in various environments. However, detecting stationary individuals and distinguishing between moving human and non-human entities remain challenging areas for future work.

Název v anglickém jazyce

Dynamic People Counting from Delay-Doppler Images in Challenging Scenarios: Enhancing Model Performance

Popis výsledku anglicky

This study presents a novel radar-based people counting (PCnt) methodology empowered by deep learning (DL) frameworks. The challenges we face include overfitting due to the model’s tendency to extract highly domain-specific features. These challenges arise from limited data and clutter in indoor settings. To tackle this, our radar system operates in both lab and industrial environments. We propose a 2D-CNN approach and explore ways to handle these challenges, focusing on im proving accuracy through preprocessing techniques. Additionally, we introduced data augmentation strategies to enhance model robustness and mitigate overfitting. Our experiments show our approach accurately counts people moving along the radar line in various environments. However, detecting stationary individuals and distinguishing between moving human and non-human entities remain challenging areas for future work.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

RADIOELEKTRONIKA 2024: 2024 34th International Conference Radioelektronika

ISBN

979-8-3503-6215-2

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

„“-„“

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

neuveden

Místo konání akce

Žilina, Slovakia

Datum konání akce

17. 4. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001229165000040