title: Robust global motion estimation oriented to video object segmentation.
abstract: Most global motion estimation (GME) methods are oriented to video coding while video object segmentation methods either assume no global motion (GM) or directly adopt a coding-oriented method to compensate for GM. This paper proposes a hierarchical differential GME method oriented to video object segmentation. A scheme which combines three-step search and motion parameters prediction is proposed for initial estimation to increase efficiency. A robust estimator that uses object information to reject outliers introduced by local motion is also proposed. For the first frame, when the object information is unavailable, a robust estimator is proposed which rejects outliers by examining their distribution in local neighborhoods of the error between the current and the motion-compensated previous frame. Subjective and objective results show that the proposed method is more robust, more oriented to video object segmentation, and faster than the referenced methods.
authors.affiliation: Electrical and Computer Engineering Department, Concordia University, Montréal, Canada. b_qi@ece.concordia.ca
authors.forename: Bin
authors.initials: B
authors.lastname: Qi
authors.forename: Mohammed
authors.initials: M
authors.lastname: Ghazal
authors.forename: Aishy
authors.initials: A
authors.lastname: Amer
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009038:Motion; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014741:Video Recording / Q000379:methods*