31,881 research outputs found
A Deep-structured Conditional Random Field Model for Object Silhouette Tracking
In this work, we introduce a deep-structured conditional random field
(DS-CRF) model for the purpose of state-based object silhouette tracking. The
proposed DS-CRF model consists of a series of state layers, where each state
layer spatially characterizes the object silhouette at a particular point in
time. The interactions between adjacent state layers are established by
inter-layer connectivity dynamically determined based on inter-frame optical
flow. By incorporate both spatial and temporal context in a dynamic fashion
within such a deep-structured probabilistic graphical model, the proposed
DS-CRF model allows us to develop a framework that can accurately and
efficiently track object silhouettes that can change greatly over time, as well
as under different situations such as occlusion and multiple targets within the
scene. Experiment results using video surveillance datasets containing
different scenarios such as occlusion and multiple targets showed that the
proposed DS-CRF approach provides strong object silhouette tracking performance
when compared to baseline methods such as mean-shift tracking, as well as
state-of-the-art methods such as context tracking and boosted particle
filtering.Comment: 17 page
Learning the dynamics and time-recursive boundary detection of deformable objects
We propose a principled framework for recursively segmenting deformable objects across a sequence
of frames. We demonstrate the usefulness of this method on left ventricular segmentation across a cardiac
cycle. The approach involves a technique for learning the system dynamics together with methods of
particle-based smoothing as well as non-parametric belief propagation on a loopy graphical model capturing
the temporal periodicity of the heart. The dynamic system state is a low-dimensional representation
of the boundary, and the boundary estimation involves incorporating curve evolution into recursive state
estimation. By formulating the problem as one of state estimation, the segmentation at each particular
time is based not only on the data observed at that instant, but also on predictions based on past and future
boundary estimates. Although the paper focuses on left ventricle segmentation, the method generalizes
to temporally segmenting any deformable object
- …