Publication Details
Automatic Camera Calibration by Landmarks on Rigid Objects
Špaňhel Jakub, Ing., Ph.D. (DCGM)
Dobeš Petr, Ing. (DCGM)
Juránek Roman, Ing., Ph.D. (DCGM)
Herout Adam, prof. Ing., Ph.D. (DCGM)
camera calibration, optimization, surveillance
This article presents a new method for automatic calibration of surveillance
cameras. We are dealing with traffic surveillance and therefore the camera is
calibrated by observing vehicles; however, other rigid objects can be used
instead. The proposed method is using keypoints or landmarks automatically
detected on the observed objects by a convolutional neural network. By using
fine-grained recognition of the vehicles (calibration objects), and by knowing
the 3D positions of the landmarks for the (very limited) set of known objects,
the extracted keypoints are used for calibration of the camera, resulting in
internal (focal length) and external (rotation, translation) parameters and scene
scale of the surveillance camera. We collected a dataset in two parking lots and
equipped it with a calibration ground truth by measuring multiple distances in
the ground plane. This dataset seems to be more accurate than the existing
comparable data (GT calibration error reduced from 4.62% to 0.99%). Also, the
experiments show that our method overcomes the best existing alternative in terms
of accuracy (error reduced from 6.56% to 4.03%) and our solution is also more
flexible in terms of viewpoint change and other.
@article{BUT168175,
author="Vojtěch {Bartl} and Jakub {Špaňhel} and Petr {Dobeš} and Roman {Juránek} and Adam {Herout}",
title="Automatic Camera Calibration by Landmarks on Rigid Objects",
journal="Machine Vision and Applications",
year="2020",
volume="32",
number="1",
pages="2--15",
doi="10.1007/s00138-020-01125-x",
issn="1432-1769",
url="https://www.fit.vut.cz/research/publication/12345/"
}