Plane chessboard-based calibration method for a LWIR ultra-wide-angle camera

Chen, Yichao; Huang, Fuyu; Shi, Fengming; Liu, Bingqi; Yu, Hao

doi:10.1364/ao.58.000744

“…There are two main strategies to build a target usable for thermal infrared camera calibration: Active targets which require partial heating or cooling for the generation of contrast have been employed for such calibrations before (Rankin et al, 2011), (Vidas et al, 2012), (Ursine et al, 2012), (Yu et al, 2013), (Zoetgnande et al, 2018), (Chen et al, 2019). Reflective targets using different emissivity surfaces are also possible, but require different temperatures for the target itself and an illumination source (Shibata et al (2017) and Aguilera et al (2018)).…”

Section: Related Workmentioning

confidence: 99%

Automatic Calibration and Co-Registration for a Stereo Camera System and a Thermal Imaging Sensor Using a Chessboard

Choinowski

¹

,

Dahlke

²

,

Ernst

³

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> This paper presents a system calibration method for a trifocal sensor, which is sensitive to different spectral bands. The trifocal camera system consists of a stereo camera, operating in the visual (VIS) spectrum and a thermal imaging camera, operating in the Long-Wave- Infrared (LWIR) spectrum. Intrinsic parameters and spatial alignment are determined simultaneously. As calibration target a passive aluminium chessboard is used. Corner detection and subsequent bundle adjustment is done on all synchronized image triplets. The remaining reprojection errors are in the sub-pixel range and enable the system to generate metric point clouds, colored with thermal intensities in real-time.</p>

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“…However, reliable and accurate camera calibration usually requires an expert intuition to reliably constrain all of the parameters in the camera model. Existing calibration toolboxes [8,9] ask users to capture images from a posed calibration pattern board (chessboard [5,10,11], circle grid pattern [12], AprilTag [13,14], etc.) in positions of their choosing, after which the maximum-likelihood calibrations parameters are computed using all images in a batch optimization.…”

Section: Introductionmentioning

confidence: 99%

Camera Calibration with Pose Guidance

Ren

¹

,

Hu

²

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

4

0

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Camera calibration plays a critical role in various computer vision tasks such as autonomous driving or augmented reality. Widely used camera calibration tools utilize plane pattern based methodology, such as using a chessboard or AprilTag board, user's calibration expertise level significantly affects calibration accuracy and consistency when without clear instruction. Furthermore, calibration is a recurring task that has to be performed each time the camera is changed or moved. It's also a great burden to calibrate huge amounts of cameras such as Driver Monitoring System (DMS) cameras in a production line with millions of vehicles. To resolve above issues, we propose a calibration system called Calibration with Pose Guidance to improve calibration accuracy, reduce calibration variance among different users or different trials of the same person. Experiment result shows that our proposed method achieves more accurate and consistent calibration than traditional calibration tools.

show abstract

“…However, reliable and accurate camera calibration usually requires an expert intuition to reliably constrain all of the parameters in the camera model. Existing calibration toolboxes [8,9] ask users to capture images from a posed calibration pattern board (chessboard [5,10,11], circle grid pattern [12], AprilTag [13,14], etc.) in positions of their choosing, after which the maximum-likelihood calibrations parameters are computed using all images in a batch optimization.…”

Section: Introductionmentioning

confidence: 99%

Camera Calibration with Pose Guidance

Ren,

Hu

2021

Preprint

0

View full text Add to dashboard Cite

Camera calibration plays a critical role in various computer vision tasks such as autonomous driving or augmented reality. Widely used camera calibration tools utilize plane pattern based methodology, such as using a chessboard or AprilTag board, user's calibration expertise level significantly affects calibration accuracy and consistency when without clear instruction. Furthermore, calibration is a recurring task that has to be performed each time the camera is changed or moved. It's also a great burden to calibrate huge amounts of cameras such as Driver Monitoring System (DMS) cameras in a production line with millions of vehicles. To resolve above issues, we propose a calibration system called Calibration with Pose Guidance to improve calibration accuracy, reduce calibration variance among different users or different trials of the same person. Experiment result shows that our proposed method achieves more accurate and consistent calibration than traditional calibration tools.

show abstract

Plane chessboard-based calibration method for a LWIR ultra-wide-angle camera

Cited by 25 publications

References 10 publications

Automatic Calibration and Co-Registration for a Stereo Camera System and a Thermal Imaging Sensor Using a Chessboard

Automatic Calibration and Co-Registration for a Stereo Camera System and a Thermal Imaging Sensor Using a Chessboard

Camera Calibration with Pose Guidance

Camera Calibration with Pose Guidance

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