Speed Measurement from Traffic Camera Video Using Structure-from-Motion-Based Calibration

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0198170" target="_blank" >RIV/00216305:26220/26:0198170 - isvavai.cz</a>

Result on the web

<a href="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2025_sbornik_2.pdf" target="_blank" >https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2025_sbornik_2.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.13164/eeict.2025.231" target="_blank" >10.13164/eeict.2025.231</a>

Result language

angličtina

Original language name

Speed Measurement from Traffic Camera Video Using Structure-from-Motion-Based Calibration

Original language description

Speed measurement from traffic camera video is an active research problem, primarily due to the challenges associated with accurate camera calibration in an uncontrolled environment. Traditional calibration techniques often require prior knowledge of the scene or manual input from the user, limiting their applicability in real-world scenarios. In this work, we propose a novel approach for traffic speed measurements using a camera calibration based on automatic 3D scene reconstruction via structure-from-motion (SfM). Our approach leverages deep learning-based feature extraction and matching, specifically SuperPoint and SuperGlue, to achieve precise scene reconstruction. By placing the camera within the reconstructed environment, we obtain its intrinsic and extrinsic parameters without requiring predefined reference objects. This allows us to establish a reliable reference for measuring distances in the scene. With this setup, we can accurately measure point distances on the ground plane, enabling robust speed estimation of moving vehicles. We present the implementation of our speed measurement method as a real-time application based on YOLO11 object detector and BoT-SORT object tracker. Our approach achieves speed measurement accuracy with an error of 6.9 km/h compared to a GPS RTK-based reference benchmark, demonstrating its effectiveness for traffic monitoring applications.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

20205 - Automation and control systems

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

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

Article name in the collection

Proceedings II of the 31th Student EEICT 2025: Selected Papers

ISBN

978-80-214-6320-2

ISSN

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e-ISSN

2788-1334

Number of pages

262

Pages from-to

246-250

Publisher name

Brno University of Technology, Faculty of Electronic Engineering and Communication

Place of publication

Brno

Event location

Brno

Event date

Apr 29, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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