Feature point classification based global motion estimation for video stabilization

Seung Kyun Kim, Seok Jae Kang, Tae Shick Wang, Sung Jea Ko

Research output: Contribution to journalArticlepeer-review

47 Citations (Scopus)


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 image1.

Original languageEnglish
Article number6490269
Pages (from-to)267-272
Number of pages6
JournalIEEE Transactions on Consumer Electronics
Issue number1
Publication statusPublished - 2013


  • Feature point classification
  • globalmotion estimation
  • video stabilization

ASJC Scopus subject areas

  • Media Technology
  • Electrical and Electronic Engineering


Dive into the research topics of 'Feature point classification based global motion estimation for video stabilization'. Together they form a unique fingerprint.

Cite this