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

The result's identifiers

  • Result code in 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>

  • Result on the web

    <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>

Alternative languages

  • Result language

    angličtina

  • Original language name

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

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

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

Result continuities

  • Project

    Result was created during the realization of more than one project. More information in the Projects tab.

  • Continuities

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

Others

  • Publication year

    2020

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

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

  • ISBN

    978-1-7281-9108-9

  • ISSN

  • e-ISSN

  • Number of pages

    8

  • Pages from-to

    1-8

  • Publisher name

    Institute of Electrical and Electronics Engineers

  • Place of publication

    Melbourne

  • Event location

    Melbourne

  • Event date

    Nov 30, 2020

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article