Ph.D. Defense: Domitille Commun

Mon Aug 09 2021 11:00 AM
"An Approach for UAV-enabled Remote Camera Calibration in Various Environments"

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Ph.D. Defense


Domitille Commun

(Advisor: Prof. Dimitri Mavris)


"An Approach for UAV-enabled Remote Camera Calibration in Various Environments"


Monday, August 9th
11:00 a.m. (EST)


Several video applications rely on camera calibration, a key enabler towards the measurement of metric parameters from images. For instance, monitoring environmental changes through remote cameras, such as glacier size changes, or measuring vehicle speed from security cameras, require cameras to be calibrated. Calibrating a camera is necessary to implement accurate computer vision techniques for the automated analysis of video footage. This automated analysis enables the ability to save cost and time in a variety of fields, such as manufacturing, civil engineering, architecture, and safety.

The large number of cameras installed and operated continues to increase. A vast portion of these cameras are “hard-to-reach” cameras.

“Hard-to-reach” cameras refer to installed cameras that cannot be removed from their location without impacting the camera parameters or the camera’s operational use. This includes remote sensing cameras or security cameras. Many of these types of cameras are not calibrated, and successfully being able to calibrate them is a key need as applications continue growing for the use of automated measurements using the video provided by the cameras.

Existing calibration methods can be divided into two groups: object-based calibration, which relies on the use of a calibration target of known dimensions, and self-calibration, which relies on the camera motion or scene geometry constraints. However, these methods have not been adapted for use with remote cameras that are hard-to-reach and have large field-of-views. Indeed, the object-based calibration method requires a tedious and manual process that is not adapted to a large field of view. Furthermore, the self-calibration requires restricted conditions to work correctly and is thus not scalable to a large type of hard-to-reach cameras, with many different parameters, and various viewing scenes.

Based on this need, the research objective of this thesis is to develop a camera calibration method for hard-to-reach cameras. The method must satisfy a series of requirements caused by the remote status of the cameras being calibrated:

  • Be adapted to large fields-of-view since these cameras cannot be accessed easily (which prevents the use of object-based calibration techniques)
  • Be scalable to various environments (which is not feasible using self-calibration techniques that require strict assumptions about the scene)
  • Be automated to enable the calibration of the large number of already installed cameras
  • Be able to correct for the large non-linear distortion that is frequently present with security cameras


In response to the calibration need, this thesis proposes a solution that relies on the use of a drone or a robot as a moving target to collect the 3D and 2D matching points required for the calibration.

The target localization in the 3D space and on the image is subject to errors, and the approach must be tested to evaluate its ability to calibrate cameras despite measurement uncertainties. This work demonstrates the success of the calibration approach using realistic simulations and real-world testing.

First, this work presents a drone trajectory that enables the collection of a complete data set. It tests the drone-based sampling strategy using simulations and shows that the use of the moving target enables the collection of a complete training set, and results in an accurate calibration. Then, this work proposes a drone design modification to improve the target detection accuracy. It evaluates the robustness of this solution in challenging conditions, such as in complex environments for the target detection.  This research also develops a strategy to evaluate the impact of camera parameters, drone path parameters, and localization uncertainties on the calibration accuracy. Applying this strategy to 5000 simulated camera models leads to recommendations for path parameters for the drone-based calibration approach and highlights the impact of camera parameters on the calibration accuracy. Additionally, this work designs and tests a method for optimizing the drone path for the remote camera calibration purpose. Finally, the knowledge gained from these experiments is applied in a real-world test, which completes the demonstration of the drone-based camera calibration approach.



  • Prof. Dimitri Mavris – School of Aerospace Engineering (advisor)
  • Prof. Cédric Pradalier – School of Interactive Computing
  • Dr. Michael Balchanos – School of Aerospace Engineering
  • Dr. Olivia Fischer – School of Aerospace Engineering
  • Prof. Graeme Kennedy – School of Aerospace Engineering