Publication detail

OptInOpt: Dual Optimization for Automatic Camera Calibration by Multi-Target Observations

BARTL, V. HEROUT, A.

Original Title

OptInOpt: Dual Optimization for Automatic Camera Calibration by Multi-Target Observations

English Title

OptInOpt: Dual Optimization for Automatic Camera Calibration by Multi-Target Observations

Type

conference paper

Language

en

Original Abstract

In this paper, we propose a new approach to automatic calibration of surveillance cameras. The proposed method is based on observing rigid objects in the scene and automatically estimating landmarks on these objects. The proposed approach can use arbitrary rigid objects, as was verified by experiments with a synthetic dataset, but vehicles were used during our experiments with real-life data. Landmarks on objects automatically detected by a convolutional neural network together with corresponding 3D positions in the object coordinate system are exploited during the camera calibration process. To determine 3D positions of the landmarks, fine-grained classification of the detected vehicles in the image plane is necessary. The proposed calibration method consists of dual optimization - optimization of objects positions in the world coordinate system and also optimization of the calibration parameters to minimize the re-projection error of the localized landmarks. The experiments show improvement in calibration accuracy over the existing method solving a similar problem furthermore with fewer restrictions on the input data. The calibration error on a real world dataset decreased from 6.88 % to 2.85 %.

English abstract

In this paper, we propose a new approach to automatic calibration of surveillance cameras. The proposed method is based on observing rigid objects in the scene and automatically estimating landmarks on these objects. The proposed approach can use arbitrary rigid objects, as was verified by experiments with a synthetic dataset, but vehicles were used during our experiments with real-life data. Landmarks on objects automatically detected by a convolutional neural network together with corresponding 3D positions in the object coordinate system are exploited during the camera calibration process. To determine 3D positions of the landmarks, fine-grained classification of the detected vehicles in the image plane is necessary. The proposed calibration method consists of dual optimization - optimization of objects positions in the world coordinate system and also optimization of the calibration parameters to minimize the re-projection error of the localized landmarks. The experiments show improvement in calibration accuracy over the existing method solving a similar problem furthermore with fewer restrictions on the input data. The calibration error on a real world dataset decreased from 6.88 % to 2.85 %.

Keywords

video surveillance, camera calibration, objects detection, classification, keypoints localization, optimization

Released

03.07.2019

Publisher

Institute of Electrical and Electronics Engineers

Location

Taipei

ISBN

978-1-7281-0990-9

Book

16th IEEE International Conference on Advanced Video and Signal-based Surveillance

Edition

NEUVEDEN

Edition number

NEUVEDEN

Pages from

1

Pages to

8

Pages count

8

Documents

BibTex


@inproceedings{BUT161456,
  author="Vojtěch {Bartl} and Adam {Herout}",
  title="OptInOpt: Dual Optimization for Automatic Camera Calibration by Multi-Target Observations",
  annote="In this paper, we propose a new approach to automatic calibration of surveillance
cameras. The proposed method is based on observing rigid objects in the scene and
automatically estimating landmarks on these objects. The proposed approach can
use arbitrary rigid objects, as was verified by experiments with a synthetic
dataset, but vehicles were used during our experiments with real-life data.
Landmarks on objects automatically detected by a convolutional neural network
together with corresponding 3D positions in the object coordinate system are
exploited during the camera calibration process. To determine 3D positions of the
landmarks, fine-grained classification of the detected vehicles in the image
plane is necessary. The proposed calibration method consists of dual optimization
- optimization of objects positions in the world coordinate system and also
optimization of the calibration parameters to minimize the re-projection error of
the localized landmarks. The experiments show improvement in calibration accuracy
over the existing method solving a similar problem furthermore with fewer
restrictions on the input data. The calibration error on a real world dataset
decreased from 6.88 % to 2.85 %.",
  address="Institute of Electrical and Electronics Engineers",
  booktitle="16th IEEE International Conference on Advanced Video and Signal-based Surveillance",
  chapter="161456",
  doi="10.1109/AVSS.2019.8909905",
  edition="NEUVEDEN",
  howpublished="online",
  institution="Institute of Electrical and Electronics Engineers",
  year="2019",
  month="july",
  pages="1--8",
  publisher="Institute of Electrical and Electronics Engineers",
  type="conference paper"
}