Projects per year
Estimating changes in camera parameters, such as motion, focal length and exposure time over a single frame or sequence of frames is an integral part of many computer vision applications. Rapid changes in these parameters often cause motion blur to be present in an image, which can make traditional methods of feature identification and tracking difficult. In this work we describe a method for tracking changes in two camera intrinsic parameters - shutter angle and scale changes brought about by changes in focal length. We also provide a method for estimating the expected accuracy of the results obtained using these methods and evaluate how the technique performs on images with a low depth of field, and therefore likely to contain blur other than that brought about by motion.
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- 1 Finished
1/09/15 → 28/02/21
Project: Research council
- Department of Computer Science - Professor
- EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa)
- Centre for the Analysis of Motion, Entertainment Research & Applications
- UKRI CDT in Accountable, Responsible and Transparent AI
- Centre for Autonomous Robotics (CENTAUR)
Person: Research & Teaching