4,430 research outputs found
Gaussian implementation of the multi-Bernoulli mixture filter
This paper presents the Gaussian implementation of the multi-Bernoulli mixture (MBM) filter. The MBM filter provides the filtering (multi-target) density for the standard dynamic and radar measurement models when the birth model is multi-Bernoulli or multi-Bernoulli mixture. Under linear/Gaussian models, the single target densities of the MBM mixture admit Gaussian closed-form expressions. Murty's algorithm is used to select the global hypotheses with highest weights. The MBM filter is compared with other algorithms in the literature via numerical simulations
Poisson multi-Bernoulli conjugate prior for multiple extended object filtering
This paper presents a Poisson multi-Bernoulli mixture (PMBM) conjugate prior
for multiple extended object filtering. A Poisson point process is used to
describe the existence of yet undetected targets, while a multi-Bernoulli
mixture describes the distribution of the targets that have been detected. The
prediction and update equations are presented for the standard transition
density and measurement likelihood. Both the prediction and the update preserve
the PMBM form of the density, and in this sense the PMBM density is a conjugate
prior. However, the unknown data associations lead to an intractably large
number of terms in the PMBM density, and approximations are necessary for
tractability. A gamma Gaussian inverse Wishart implementation is presented,
along with methods to handle the data association problem. A simulation study
shows that the extended target PMBM filter performs well in comparison to the
extended target d-GLMB and LMB filters. An experiment with Lidar data
illustrates the benefit of tracking both detected and undetected targets
Efficient Approximation of the Labeled Multi-Bernoulli Filter for Online Multitarget Tracking
Online tracking time-varying number of targets is a challenging issue due to measurement noise, target birth or death, and association uncertainty, especially when target number is large. In this paper, we propose an efficient approximation of the Labeled Multi-Bernoulli (LMB) filter to perform online multitarget state estimation and track maintenance efficiently. On the basis of the original LMB filer, we propose a target posterior approximation technique to use a weighted single Gaussian component representing each individual target. Moreover, we present the Gaussian mixture implementation of the proposed efficient approximation of the LMB filter under linear, Gaussian assumptions on the target dynamic model and measurement model. Numerical results verify that our proposed efficient approximation of the LMB filer achieves accurate tracking performance and runs several times faster than the original LMB filer
Extended Object Tracking: Introduction, Overview and Applications
This article provides an elaborate overview of current research in extended
object tracking. We provide a clear definition of the extended object tracking
problem and discuss its delimitation to other types of object tracking. Next,
different aspects of extended object modelling are extensively discussed.
Subsequently, we give a tutorial introduction to two basic and well used
extended object tracking approaches - the random matrix approach and the Kalman
filter-based approach for star-convex shapes. The next part treats the tracking
of multiple extended objects and elaborates how the large number of feasible
association hypotheses can be tackled using both Random Finite Set (RFS) and
Non-RFS multi-object trackers. The article concludes with a summary of current
applications, where four example applications involving camera, X-band radar,
light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are
highlighted.Comment: 30 pages, 19 figure
Multi-Object Tracking with Interacting Vehicles and Road Map Information
In many applications, tracking of multiple objects is crucial for a
perception of the current environment. Most of the present multi-object
tracking algorithms assume that objects move independently regarding other
dynamic objects as well as the static environment. Since in many traffic
situations objects interact with each other and in addition there are
restrictions due to drivable areas, the assumption of an independent object
motion is not fulfilled. This paper proposes an approach adapting a
multi-object tracking system to model interaction between vehicles, and the
current road geometry. Therefore, the prediction step of a Labeled
Multi-Bernoulli filter is extended to facilitate modeling interaction between
objects using the Intelligent Driver Model. Furthermore, to consider road map
information, an approximation of a highly precise road map is used. The results
show that in scenarios where the assumption of a standard motion model is
violated, the tracking system adapted with the proposed method achieves higher
accuracy and robustness in its track estimations
Marginal multi-Bernoulli filters: RFS derivation of MHT, JIPDA and association-based MeMBer
Recent developments in random finite sets (RFSs) have yielded a variety of
tracking methods that avoid data association. This paper derives a form of the
full Bayes RFS filter and observes that data association is implicitly present,
in a data structure similar to MHT. Subsequently, algorithms are obtained by
approximating the distribution of associations. Two algorithms result: one
nearly identical to JIPDA, and another related to the MeMBer filter. Both
improve performance in challenging environments.Comment: Journal version at http://ieeexplore.ieee.org/document/7272821.
Matlab code of simple implementation included with ancillary file
Poisson multi-Bernoulli mixture trackers: continuity through random finite sets of trajectories
The Poisson multi-Bernoulli mixture (PMBM) is an unlabelled multi-target
distribution for which the prediction and update are closed. It has a Poisson
birth process, and new Bernoulli components are generated on each new
measurement as a part of the Bayesian measurement update. The PMBM filter is
similar to the multiple hypothesis tracker (MHT), but seemingly does not
provide explicit continuity between time steps. This paper considers a recently
developed formulation of the multi-target tracking problem as a random finite
set (RFS) of trajectories, and derives two trajectory RFS filters, called PMBM
trackers. The PMBM trackers efficiently estimate the set of trajectories, and
share hypothesis structure with the PMBM filter. By showing that the prediction
and update in the PMBM filter can be viewed as an efficient method for
calculating the time marginals of the RFS of trajectories, continuity in the
same sense as MHT is established for the PMBM filter
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