title: Multiple collaborative kernel tracking.
abstract: Those motion parameters that cannot be recovered from image measurements are unobservable in the visual dynamic system. This paper studies this important issue of singularity in the context of kernel-based tracking and presents a novel approach that is based on a motion field representation which employs redundant but sparsely correlated local motion parameters instead of compact but uncorrelated global ones. This approach makes it easy to design fully observable kernel-based motion estimators. This paper shows that these high-dimensional motion fields can be estimated efficiently by the collaboration among a set of simpler local kernel-based motion estimators, which makes the new approach very practical.
authors.affiliation: Electrical Engineering and Computer Science Department, Northwestern University, Evanston, IL 60208, USA. zfa825@ece.northwestern.edu
authors.forename: Zhimin
authors.initials: Z
authors.lastname: Fan
authors.forename: Ming
authors.initials: M
authors.lastname: Yang
authors.forename: Ying
authors.initials: Y
authors.lastname: Wu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D009038:Motion; D009068:Movement; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity