PlaneCalib: Automatic Camera Calibration by Multiple Observations of Rigid Objects on Plane

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F20%3APU138876" target="_blank" >RIV/00216305:26230/20:PU138876 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.dicta2020.org/wp-content/uploads/2020/09/58_CameraReady.pdf" target="_blank" >http://www.dicta2020.org/wp-content/uploads/2020/09/58_CameraReady.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/DICTA51227.2020.9363417" target="_blank" >10.1109/DICTA51227.2020.9363417</a>

Jazyk výsledku

angličtina

Název v původním jazyce

PlaneCalib: Automatic Camera Calibration by Multiple Observations of Rigid Objects on Plane

Popis výsledku v původním jazyce

In this work, we propose a novel method for automatic camera calibration, mainly for surveillance cameras. The calibration consists in observing objects on the ground plane of the scene; in our experiments, vehicles were used. However, any arbitrary rigid objects can be used instead, as verified by experiments with synthetic data. The calibration process uses convolutional neural network localisation of landmarks on the observed objects in the scene and the corresponding 3D positions of the localised landmarks - thus fine-grained classification of the detected vehicles in the image plane is done. The observation of the objects (detection, classification and landmark detection) enables to determine all typically used camera calibration parameters (focal length, rotation matrix, and translation vector). The experiments with real data show slightly better results in comparison with state-of-the-art work, however with an extreme speed-up. The calibration error decreased from 3.01 % to 2.72 % and 1223 × faster computation was achieved.

Název v anglickém jazyce

PlaneCalib: Automatic Camera Calibration by Multiple Observations of Rigid Objects on Plane

Popis výsledku anglicky

In this work, we propose a novel method for automatic camera calibration, mainly for surveillance cameras. The calibration consists in observing objects on the ground plane of the scene; in our experiments, vehicles were used. However, any arbitrary rigid objects can be used instead, as verified by experiments with synthetic data. The calibration process uses convolutional neural network localisation of landmarks on the observed objects in the scene and the corresponding 3D positions of the localised landmarks - thus fine-grained classification of the detected vehicles in the image plane is done. The observation of the objects (detection, classification and landmark detection) enables to determine all typically used camera calibration parameters (focal length, rotation matrix, and translation vector). The experiments with real data show slightly better results in comparison with state-of-the-art work, however with an extreme speed-up. The calibration error decreased from 3.01 % to 2.72 % and 1223 × faster computation was achieved.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

2020 International Conference on Digital Image Computing: Techniques and Applications (DICTA)

ISBN

978-1-7281-9108-9

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

1-8

Název nakladatele

Institute of Electrical and Electronics Engineers

Místo vydání

Melbourne

Místo konání akce

Melbourne

Datum konání akce

30. 11. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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