Robust Learning-Based Camera Motion Characterization Scheme With Applications to Video Stabilization

Author(s):  
Manish Okade ◽  
Gaurav Patel ◽  
Prabir Kumar Biswas
2006 ◽  
Vol 8 (2) ◽  
pp. 323-340 ◽  
Author(s):  
Ling-Yu Duan ◽  
J.S. Jin ◽  
Qi Tian ◽  
Chang-Sheng Xu

2019 ◽  
Author(s):  
Marcos Roberto e Souza ◽  
Helio Pedrini

Several devices have allowed the acquisition and editing ofvideos in various circumstances, such as digital cameras, smartphones and other mobile devices. However, the use ofcameras under adverse conditions usually results in non-precise motion and occurrence of shaking, which may compromise the stability of the obtained videos. To overcome such problem, digital stabiliza- tion aims to correct camera motion oscillations that occur in the acquisition process, particularly when the cameras are mobile and handled in adverse con- ditions, through software techniques, without the use of specific hardware, to enhance visual quality either with the intention of enhancing human percep- tion or improving final applications, such as detection and tracking of objects. This is important in order to avoid hardware cost and indispensable for videos already recorded. This work proposed three methods to perform digital video stabilization and two other techniques to evaluate video stabilization quality. 1.


2017 ◽  
Vol 29 (3) ◽  
pp. 566-579 ◽  
Author(s):  
Sarthak Pathak ◽  
◽  
Alessandro Moro ◽  
Hiromitsu Fujii ◽  
Atsushi Yamashita ◽  
...  

[abstFig src='/00290003/12.jpg' width='300' text='Spherical video stabilization' ] We propose a method for stabilizing spherical videos by estimating and removing the effect of camera rotation using dense optical flow fields. By derotating each frame in the video to the orientation of its previous frame in two dense approaches, we estimate the complete 3 DoF rotation of the camera and remove it to stabilize the spherical video. Following this, any chosen area on the spherical video (equivalent of a normal camera’s field of view) is unwarped to result in a ‘rotation-less virtual camera’ that can be oriented independent of the camera motion. This can help in perception of the environment and camera motion much better. In order to achieve this, we use dense optical flow, which can provide important information about camera motion in a static environment and can have several advantages over sparse feature-point based approaches. The spatial regularization property of dense optical flow provides more stable motion information as compared to tracking sparse points and negates the effect of feature point outliers. We show superior results as compared to using sparse feature points alone.


1997 ◽  
Author(s):  
Ruggero Milanese ◽  
Frederic Deguillaume ◽  
Alain Jacot-Descombes

2019 ◽  
Author(s):  
Marcos Roberto Souza ◽  
Helio Pedrini

Several devices have allowed the acquisition and editing of videos in various circumstances, such as digital cameras, smartphones and other mobile devices. However, the use of cameras under adverse conditions usually results in non-precise motion and occurrence of shaking, which may compromise the stability of the obtained videos. To overcome such problem, digital stabilization aims to correct camera motion oscillations that occur in the acquisition process, particularly when the cameras are mobile and handled in adverse conditions, through software techniques - without the use of specific hardware - to enhance visual quality either with the intention of enhancing human perception or improving final applications, such as detection and tracking of objects. This is important in order to avoid hardware cost and indispensable for videos already recorded. This work proposed three methods to perform digital video stabilization and two other techniques to evaluate video stabilization quality.


Sign in / Sign up

Export Citation Format

Share Document