Generalized pupil-centric imaging and analytical calibration for a non-frontal camera

Avinash Kumar, Narendra Ahuja

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We consider the problem of calibrating a small field of view central perspective non-frontal camera whose lens and sensor planes may not be parallel to each other. This can be due to manufacturing defects or intentional tilting. Thus, as such all cameras can be modeled as being non-frontal with varying degrees. There are two approaches to model non- frontal cameras. The first one based on rotation parameter- ization of sensor non-frontalness/tilt increases the number of calibration parameters, thus requiring heuristics to ini- tialize a few calibration parameters for the final non-linear optimization step. Additionally, for this parameterization, while it has been shown that pupil-centric imaging model leads to more accurate rotation estimates than a thin-lens imaging model, it has only been developed for a single axis lens-sensor tilt. But, in real cameras we can have arbitrary tilt. The second approach based on decentering distortion modeling is approximate as it can only handle small tilts and cannot explicitly estimate the sensor tilt. In this paper, we focus on rotation based non-frontal camera calibration and address the aforementioned prob- lems of over-parameterization and inadequacy of existing pupil-centric imaging model. We first derive a generalized pupil-centric imaging model for arbitrary axis lens-sensor tilt. We then derive an analytical solution, in this setting, for a subset of calibration parameters including sensor rotation angles as a function of center of radial distortion (CoD). A radial alignment based constraint is then proposed to com- putationally estimate CoD leveraging on the proposed an- alytical solution. Our analytical technique also estimates pupil-centric parameters of entrance pupil location and op- tical focal length, which have typically been done opti- cally. Given these analytical and computational calibration parameter estimates, we initialize the non-linear calibra- tion optimization for a set of synthetic and real data cap- tured from a non-frontal camera and show reduced pixel re-projection and undistortion errors compared to state of the art techniques in rotation and decentering based ap- proaches to non-frontal camera calibration.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
PublisherIEEE Computer Society
Pages3970-3977
Number of pages8
ISBN (Electronic)9781479951178, 9781479951178
DOIs
StatePublished - Sep 24 2014
Event27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014 - Columbus, United States
Duration: Jun 23 2014Jun 28 2014

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Other

Other27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014
CountryUnited States
CityColumbus
Period6/23/146/28/14

Keywords

  • analytical
  • calibration
  • center of radial distortion
  • pupil-centric

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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