.png){kind=icon}
Return
Feature Point Classification Based Global Motion Estimation for Video Stabilization
.png){kind=icon}
PRE
AI
DOI：10.1109/TCE.2013.6490269.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The performance of video stabilization is dependent on the accuracy of global motion estimation between two successive frames. In this paper, we propose a novel method to estimate the global motion accurately using the classified background (BG) feature points (FPs). In the proposed method, global motion estimation and FP classification are jointly performed using both the FP correspondences and the global motion parameters of the previous frame. The experimental results show that video stabilization using the proposed method outperforms the conventional stabilization methods, especially when the moving foreground (FG) objects occupy a large part of the image(1).
Keywords:
Feature point classification
global motion estimation
video stabilization
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
10.9
Papers:
5.3K
Citations:
6.8K
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSave
View more