A Central Sub-image Based Global Motion Estimation Method for In-Car Video Stabilization

2010 ◽  
Author(s):  
Yuefei Zhang ◽  
Mei Xie ◽  
Dongming Tang
Keyword(s):  
Motion Estimation ◽  
Estimation Method ◽  
Video Stabilization ◽  
Global Motion ◽  
Global Motion Estimation

Feature point classification based global motion estimation for video stabilization

2013 ◽  
Vol 59 (1) ◽  
pp. 267-272 ◽  
Author(s):  
Seung-Kyun Kim ◽  
Seok-Jae Kang ◽  
Tae-Shick Wang ◽  
Sung-Jea Ko
Keyword(s):  
Motion Estimation ◽  
Feature Point ◽  
Video Stabilization ◽  
Global Motion ◽  
Global Motion Estimation

scholarly journals Robust Global Motion Estimation for Video Stabilization Based on Improved K-Means Clustering and Superpixel

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2505
Author(s):  
Rouwan Wu ◽  
Zhiyong Xu ◽  
Jianlin Zhang ◽  
Lihong Zhang
Keyword(s):  
Motion Estimation ◽  
Motion Vector ◽  
Similarity Index ◽  
Video Frame ◽  
Cluster Center ◽  
Video Stabilization ◽  
Feature Points ◽  
Global Motion ◽  
Simple Method ◽  
Global Motion Estimation

Obtaining accurate global motion is a crucial step for video stabilization. This paper proposes a robust and simple method to implement global motion estimation. We don’t extend the framework of 2D video stabilization but add a “plug and play” module to motion estimation based on feature points. Firstly, simple linear iterative clustering (SLIC) pre-segmentation is used to obtain superpixels of the video frame, clustering is performed according to the superpixel centroid motion vector and cluster center with large value is eliminated. Secondly, in order to obtain accurate global motion estimation, an improved K-means clustering is proposed. We match the feature points of the remaining superpixels between two adjacent frames, establish a feature points’ motion vector space, and use improved K-means clustering for clustering. Finally, the richest cluster is being retained, and the global motion is obtained by homography transformation. Our proposed method has been verified on different types of videos and has efficient performance than traditional approaches. The stabilization video has an average improvement of 0.24 in the structural similarity index than the original video and 0.1 higher than the traditional method.

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