Investigating camera calibration methods for naturalistic driving studies

Jeffrey Paone; Thomas Karnowski; Deniz Aykac; Regina Ferrell; Jim Goddard; Austin Albright

doi:10.2352/ISSN.2470-1173.2019.7.IRIACV-461

Investigating camera calibration methods for naturalistic driving studies

Jeffrey Paone, Thomas Karnowski, Deniz Aykac, Regina Ferrell, Jim Goddard, Austin Albright

Research output: Contribution to journal › Conference article › peer-review

2 Scopus citations

Abstract

Naturalistic driving studies typically utilize a variety of sensors, including radar, kinematic sensors, and video cameras. While the main objective of such sensors is typically safety focused, with a goal of recording accidents and near accidents for later review, the instrumentation provides a valuable resource for a variety of transportation research. Some applications, however, require additional processing to improve the utility of the data. In this work, we describe a computer vision procedure for calibrating front view cameras for the Second Strategic Highway Research Project. A longitudinal stability study of the estimated parameters across a small sample set of cameras is presented along with a proposed procedure for calibrating a larger number of cameras from the study. A simple use case is presented as one example of the utility of this work. Finally, we discuss plans for calibrating the complete set of approximately 3000 cameras from this study.

Original language	English
Article number	461
Journal	IS and T International Symposium on Electronic Imaging Science and Technology
Volume	2019
Issue number	7
DOIs	https://doi.org/10.2352/ISSN.2470-1173.2019.7.IRIACV-461
State	Published - Jan 13 2019
Event	2019 Intelligent Robotics and Industrial Applications Using Computer Vision Conference, IRIACV 2019 - Burlingame, United States Duration: Jan 13 2019 → Jan 17 2019

Funding

We would like to acknowledge the support and assistance of Virginia Tech Transportation Institute, especially Jon Hankey, Jon Antin, Suzanne Lee and Miguel Perez. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). Work was funded by the Federal Highway Administration of the US Department of Transportation, Exploratory Advanced Research Fund. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). Work was funded by the Federal Highway Administration of the US Department of Transportation, Exploratory Advanced Research Fund.

Keywords

Camera calibration
Computer vision
Naturalistic driving studies
Sensor fusion

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10.2352/ISSN.2470-1173.2019.7.IRIACV-461

Cite this

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title = "Investigating camera calibration methods for naturalistic driving studies",

abstract = "Naturalistic driving studies typically utilize a variety of sensors, including radar, kinematic sensors, and video cameras. While the main objective of such sensors is typically safety focused, with a goal of recording accidents and near accidents for later review, the instrumentation provides a valuable resource for a variety of transportation research. Some applications, however, require additional processing to improve the utility of the data. In this work, we describe a computer vision procedure for calibrating front view cameras for the Second Strategic Highway Research Project. A longitudinal stability study of the estimated parameters across a small sample set of cameras is presented along with a proposed procedure for calibrating a larger number of cameras from the study. A simple use case is presented as one example of the utility of this work. Finally, we discuss plans for calibrating the complete set of approximately 3000 cameras from this study.",

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AU - Karnowski, Thomas

AU - Aykac, Deniz

AU - Ferrell, Regina

AU - Goddard, Jim

AU - Albright, Austin

PY - 2019/1/13

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N2 - Naturalistic driving studies typically utilize a variety of sensors, including radar, kinematic sensors, and video cameras. While the main objective of such sensors is typically safety focused, with a goal of recording accidents and near accidents for later review, the instrumentation provides a valuable resource for a variety of transportation research. Some applications, however, require additional processing to improve the utility of the data. In this work, we describe a computer vision procedure for calibrating front view cameras for the Second Strategic Highway Research Project. A longitudinal stability study of the estimated parameters across a small sample set of cameras is presented along with a proposed procedure for calibrating a larger number of cameras from the study. A simple use case is presented as one example of the utility of this work. Finally, we discuss plans for calibrating the complete set of approximately 3000 cameras from this study.

AB - Naturalistic driving studies typically utilize a variety of sensors, including radar, kinematic sensors, and video cameras. While the main objective of such sensors is typically safety focused, with a goal of recording accidents and near accidents for later review, the instrumentation provides a valuable resource for a variety of transportation research. Some applications, however, require additional processing to improve the utility of the data. In this work, we describe a computer vision procedure for calibrating front view cameras for the Second Strategic Highway Research Project. A longitudinal stability study of the estimated parameters across a small sample set of cameras is presented along with a proposed procedure for calibrating a larger number of cameras from the study. A simple use case is presented as one example of the utility of this work. Finally, we discuss plans for calibrating the complete set of approximately 3000 cameras from this study.

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KW - Sensor fusion

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Investigating camera calibration methods for naturalistic driving studies

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