11,503 research outputs found
Multisensor Multiobject Tracking With High-Dimensional Object States
Passive monitoring of acoustic or radio sources has important applications in
modern convenience, public safety, and surveillance. A key task in passive
monitoring is multiobject tracking (MOT). This paper presents a Bayesian method
for multisensor MOT for challenging tracking problems where the object states
are high-dimensional, and the measurements follow a nonlinear model. Our method
is developed in the framework of factor graphs and the sum-product algorithm
(SPA) and implemented using random samples or "particles". The multimodal
probability density functions (pdfs) provided by the SPA are effectively
represented by a Gaussian mixture model (GMM). To perform the operations of the
SPA in high-dimensional spaces, we make use of Particle flow (PFL). Here,
particles are migrated towards regions of high likelihood based on the solution
of a partial differential equation. This makes it possible to obtain good
object detection and tracking performance even in challenging multisensor MOT
scenarios with single sensor measurements that have a lower dimension than the
object positions. We perform a numerical evaluation in a passive acoustic
monitoring scenario where multiple sources are tracked in 3-D from 1-D
time-difference-of-arrival (TDOA) measurements provided by pairs of
hydrophones. Our numerical results demonstrate favorable detection and
estimation accuracy compared to state-of-the-art reference techniques.Comment: 13 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
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