A robust estimation method for camera calibration with known rotation. Issue 1 (21st October 2015)
- Record Type:
- Journal Article
- Title:
- A robust estimation method for camera calibration with known rotation. Issue 1 (21st October 2015)
- Main Title:
- A robust estimation method for camera calibration with known rotation
- Authors:
- Egozi, Amir
Maass, Peter
Sagiv, Chen - Abstract:
- <abstract abstract-type="main" xml:lang="en"> <title>Abstract</title> <p>Imagine that hundreds of video streams, taken by mobile phones during a rock concert, are uploaded to a server. One attractive application of such prominent dataset is to allow a user to create his own video with a deliberately chosen but virtual camera trajectory. This is the challenge addressed by the Scenenet project, programme FET‐Open SME (GA 309169). One of the most basic tasks related to creating such a video, is the spatial registration of the individual video stream prior to combining them into a new virtual video. At its core, this requires to estimate the pairwise relative geometry of images taken by different cameras. This is known as the relative pose problem [1], and is fundamental to many computer vision algorithms. In practice, efficiency and robustness is of highest relevance for big data applications as the ones address in the EU‐FET_SME project Scenenet. In this paper, we present an improved algorithm that exploits additional data from inertial sensors, such as accelerometer, magnetometer or gyroscopes, which by now are available in most mobile phones. We demonstrate that in this case the pose estimation problem reduces to a 3‐dimensional least‐squares optimization problem, which can be readily implemented into a 3‐points RANSAC scheme. Experimental results on synthetic and real data demonstrate the accuracy and efficiency of our algorithm. (© 2015 Wiley‐VCH Verlag GmbH &amp; Co.<abstract abstract-type="main" xml:lang="en"> <title>Abstract</title> <p>Imagine that hundreds of video streams, taken by mobile phones during a rock concert, are uploaded to a server. One attractive application of such prominent dataset is to allow a user to create his own video with a deliberately chosen but virtual camera trajectory. This is the challenge addressed by the Scenenet project, programme FET‐Open SME (GA 309169). One of the most basic tasks related to creating such a video, is the spatial registration of the individual video stream prior to combining them into a new virtual video. At its core, this requires to estimate the pairwise relative geometry of images taken by different cameras. This is known as the relative pose problem [1], and is fundamental to many computer vision algorithms. In practice, efficiency and robustness is of highest relevance for big data applications as the ones address in the EU‐FET_SME project Scenenet. In this paper, we present an improved algorithm that exploits additional data from inertial sensors, such as accelerometer, magnetometer or gyroscopes, which by now are available in most mobile phones. We demonstrate that in this case the pose estimation problem reduces to a 3‐dimensional least‐squares optimization problem, which can be readily implemented into a 3‐points RANSAC scheme. Experimental results on synthetic and real data demonstrate the accuracy and efficiency of our algorithm. (© 2015 Wiley‐VCH Verlag GmbH &amp; Co. KGaA, Weinheim)</p> </abstract> … (more)
- Is Part Of:
- Proceedings in applied mathematics and mechanics. Volume 15:Issue 1(2015)
- Journal:
- Proceedings in applied mathematics and mechanics
- Issue:
- Volume 15:Issue 1(2015)
- Issue Display:
- Volume 15, Issue 1 (2015)
- Year:
- 2015
- Volume:
- 15
- Issue:
- 1
- Issue Sort Value:
- 2015-0015-0001-0000
- Page Start:
- 657
- Page End:
- 658
- Publication Date:
- 2015-10-21
- Subjects:
- Applied mathematics -- Periodicals
Engineering mathematics -- Periodicals
Mathematical physics -- Periodicals
519 - Journal URLs:
- http://www.onlinelibrary.wiley.com/journal/10.1002/(ISSN)1617-7061 ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1002/pamm.201510318 ↗
- Languages:
- English
- ISSNs:
- 1617-7061
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 6842.471350
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 3538.xml