Domain-Shift Manifold Online Learning and Tracking of Video Objects
Konferensbidrag (offentliggjort, men ej förlagsutgivet), 2013
This paper describes a novel Grassmann manifold
object tracking scheme that includes the modules of
manifold online learning and occlusion handling. When
objects contain significant out-of-plane pose changes, the
domain where object appearances lying is shifting with
time, hence a single vector space is no longer suitable for
dynamic object representation.Motivated by this, we present
a manifold-based scheme for tracking large out-of-plane
objects (i.e. camera is close to the object) in video with
online learning and long-term partial occlusion modules.
The tracker uses Bayesian formulation on the manifold, performing posterior state estimation based on nonlinear state space modeling. One particle filter is applied for manifold online learning, another is for tracking. Occlusion handling is applied during the online learning to prevent learning occluding object/clutter. Tests on videos have shown very robust tracking performance when objects contain significant out-of-plane pose changes accompanied with long-term partial occlusions. Comparisons with two existing methods provide further support to the proposed method.
visual object tracking
domain-shift online learning