Open AccessVideo stabilisation based on modelling of motion imaging
Author(s) -
Yu Jialin,
Xiang Ke,
Wang Xuanyin,
Cao Songxiao,
Zhang Yang
Publication year - 2016
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2015.0321
Subject(s) - computer vision , computer science , artificial intelligence , motion estimation , translation (biology) , motion (physics) , rotation (mathematics) , motion vector , jitter , quarter pixel motion , filter (signal processing) , image stabilization , motion compensation , image (mathematics) , telecommunications , biochemistry , chemistry , messenger rna , gene
This study proposes a novel digital video stabilisation scheme based on modelling of motion imaging (MI). The modelling of MI eliminates the speed motion as a result of a moving car, which is ignored in other models such as rotation + translation model, and estimates movement parameters of the background in video sequences captured from cameras mounted on moving cars. The authors first analyse the MI to understand the principle of the effects of car motion on MI, and select the matching method according to the proposed model. Then, they employ symmetric points to remove the speed motion. Finally, unwanted motion vector is stimulated by employing adaptive step‐length filter, and the boundary compensating approach is employed to suppress the image jitter effectively. Their major contribution is the elimination of the effect of carrier's speed in motion estimation. Other contributions include new robust block matching approach and adaptive‐step selection for motion filtering. They conduct experiments on real videos and artificial data. Experiments on real videos show that the proposed model can remove the effect of car motion, whereas the experiments on artificial data are conducted for theoretical analysis.