Publication Details
PlaneCalib: Automatic Camera Calibration by Multiple Observations of Rigid Objects on Plane
Juránek Roman, Ing., Ph.D. (DCGM)
Špaňhel Jakub, Ing., Ph.D. (DCGM)
Herout Adam, prof. Ing., Ph.D. (DCGM)
Camera calibration, surveillance camera, keypoint detection, object detection
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.
@inproceedings{BUT168489,
author="Vojtěch {Bartl} and Roman {Juránek} and Jakub {Špaňhel} and Adam {Herout}",
title="PlaneCalib: Automatic Camera Calibration by Multiple Observations of Rigid Objects on Plane",
booktitle="2020 International Conference on Digital Image Computing: Techniques and Applications (DICTA)",
year="2020",
pages="1--8",
publisher="Institute of Electrical and Electronics Engineers",
address="Melbourne",
doi="10.1109/DICTA51227.2020.9363417",
isbn="978-1-7281-9108-9",
url="http://www.dicta2020.org/wp-content/uploads/2020/09/58_CameraReady.pdf"
}