Publication Details
PlaneCalib: Automatic Camera Calibration by Multiple Observations of Rigid Objects on Plane
Juránek Roman, Ing., Ph.D. (DCGM FIT BUT)
Špaňhel Jakub, Ing., Ph.D. (DCGM FIT BUT)
Herout Adam, prof. Ing., Ph.D. (DCGM FIT BUT)
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{FITPUB12352, author = "Vojt\v{e}ch Bartl and Roman Jur\'{a}nek and Jakub \v{S}pa\v{n}hel and Adam Herout", title = "PlaneCalib: Automatic Camera Calibration by Multiple Observations of Rigid Objects on Plane", pages = "1--8", booktitle = "2020 International Conference on Digital Image Computing: Techniques and Applications (DICTA)", year = 2020, location = "Melbourne, AU", publisher = "Institute of Electrical and Electronics Engineers", ISBN = "978-1-7281-9108-9", doi = "10.1109/DICTA51227.2020.9363417", language = "english", url = "https://www.fit.vut.cz/research/publication/12352" }