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Gyroscope-based Video Stabilisation With Auto-Calibration

Hannes Ovrén, Per-Erik Forssén
ICRA15, Seattle, USA
IEEE International Conference on Robotics and Automation ICRA'15
May 2015


We propose a technique for joint calibration of a wide-angle rolling shutter camera (e.g.~a GoPro) and an externally mounted gyroscope. The calibrated parameters are time scaling and offset, relative pose between gyroscope and camera, and gyroscope bias. The parameters are found using non-linear least squares minimisation using the symmetric transfer error as cost function.
The primary contribution is methods for robust initialisation of the relative pose and time offset, which are essential for convergence. We also introduce a robust error norm to handle outliers. This results in a technique that works with general video content and does not require any specific setup or calibration patterns.
We apply our method to stabilisation of videos recorded by a rolling shutter camera, with a rigidly attached gyroscope. After recording, the gyroscope and camera are jointly calibrated using the recorded video itself. The recorded video can then be stabilised using the calibrated parameters.
We evaluate the technique on video sequences with varying difficulty and motion frequency content. The experiments demonstrate that our method can be used to produce high quality stabilised videos even under difficult conditions, and that the proposed initialisation is shown to end up within the basin of attraction. We also show that a residual based on the symmetric transfer error is more accurate than residuals based on the recently proposed epipolar plane normal coplanarity constraint, and that the use of robust errors is a critical component to obtain an accurate calibration.

Full Paper

Portable document format file PDF ()
Link to dataset.
Supplemental video.

Bibtex entry

  author = 	 {Hannes Ovr\'en and Per-Erik Forss\'en},
  title = 	 {Gyroscope-based Video Stabilisation With Auto-Calibration},
  booktitle =    {{IEEE} International Conference on Robotics and Automation {ICRA'15}},
  year = 	 {2015},
  month = 	 {May},
  address = 	 {Seattle, USA},
  publisher =    {{IEEE}},
  note =         {VR Project: Learnable Camera Motion Models, 2014-5928, SSF Project: The Virtual Photo Set, IIS11-0081}

Per-Erik Forssén

Per-Erik Forssén


Computer Vision Laboratory
Department of Electrical Engineering
Building B
Room 2A:519
SE-581 83 Linköping, Sweden
+46(0)13 285654

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Informationsansvarig: Per-Erik Forssén
Senast uppdaterad: 2017-02-01