Automatic Traffic Camera Calibration Using 3D Scene Reconstruction with Structure-from-Motion and Custom Image Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0198171" target="_blank" >RIV/00216305:26220/26:0198171 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Automatic Traffic Camera Calibration Using 3D Scene Reconstruction with Structure-from-Motion and Custom Image Data

Popis výsledku v původním jazyce

Traffic cameras play a crucial role in monitoring and managing transportation systems, and accurate calibration is essential for reliable data acquisition. Traditional calibration methods often require manual interventions, physical markers, or specific tools, which can be both time-consuming and costly, especially in complex urban environments. To overcome these challenges, recent methods use the 3D reconstruction of the scene using the Structure-from-Motion (SfM) techniques. This allows for a precise traffic camera localization in the model and then finding the intrinsic and extrinsic camera parameters. However, these methods typically rely on high-quality image data from public sources such as Google Streeve View (GSV), which does not always provide images that are up to date or sometimes are not available for a given location. In this paper, we introduce a new approach of automatic camera calibration via the SfM techniques using our own data. Testing was conducted using a custom dataset consisting of images taken from various angles, followed by the 3D reconstruction and calibration process. We evaluated our approach on the task of distance measurements in the view of the traffic camera. The results demonstrate an accuracy of measurements with an error margin of 1.03 meter, showcasing the effectiveness of the proposed method in real world applications.

Název v anglickém jazyce

Automatic Traffic Camera Calibration Using 3D Scene Reconstruction with Structure-from-Motion and Custom Image Data

Popis výsledku anglicky

Traffic cameras play a crucial role in monitoring and managing transportation systems, and accurate calibration is essential for reliable data acquisition. Traditional calibration methods often require manual interventions, physical markers, or specific tools, which can be both time-consuming and costly, especially in complex urban environments. To overcome these challenges, recent methods use the 3D reconstruction of the scene using the Structure-from-Motion (SfM) techniques. This allows for a precise traffic camera localization in the model and then finding the intrinsic and extrinsic camera parameters. However, these methods typically rely on high-quality image data from public sources such as Google Streeve View (GSV), which does not always provide images that are up to date or sometimes are not available for a given location. In this paper, we introduce a new approach of automatic camera calibration via the SfM techniques using our own data. Testing was conducted using a custom dataset consisting of images taken from various angles, followed by the 3D reconstruction and calibration process. We evaluated our approach on the task of distance measurements in the view of the traffic camera. The results demonstrate an accuracy of measurements with an error margin of 1.03 meter, showcasing the effectiveness of the proposed method in real world applications.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/CK04000027" target="_blank" >CK04000027: Systém řízENí Dopravy nové gEneRace (SENDER)</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

ICUMT 2024; 16th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops

ISBN

978-3-8007-6544-7

ISSN

—

e-ISSN

2157-023X

Počet stran výsledku

6

Strana od-do

19-24

Název nakladatele

—

Místo vydání

—

Místo konání akce

Meloneras, Gran Canaria, Spain

Datum konání akce

26. 11. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—