24,776 research outputs found
An Implementation of the Poisson Multi-Bernoulli Mixture Trajectory Filter via Dual Decomposition
This paper proposes an efficient implementation of the Poisson
multi-Bernoulli mixture (PMBM) trajectory filter. The proposed implementation
performs track-oriented N-scan pruning to limit complexity, and uses dual
decomposition to solve the involved multi-frame assignment problem. In contrast
to the existing PMBM filter for sets of targets, the PMBM trajectory filter is
based on sets of trajectories which ensures that track continuity is formally
maintained. The resulting filter is an efficient and scalable approximation to
a Bayes optimal multi-target tracking algorithm, and its performance is
compared, in a simulation study, to the PMBM target filter, and the delta
generalized labelled multi-Bernoulli filter, in terms of state/trajectory
estimation error and computational time.Comment: 8 pages, 2018 21st International Conference on Information Fusion
(FUSION
Multiple target tracking based on sets of trajectories
We propose a solution of the multiple target tracking (MTT) problem based on
sets of trajectories and the random finite set framework. A full Bayesian
approach to MTT should characterise the distribution of the trajectories given
the measurements, as it contains all information about the trajectories. We
attain this by considering multi-object density functions in which objects are
trajectories. For the standard tracking models, we also describe a conjugate
family of multitrajectory density functions.Comment: MATLAB implementations of algorithms based on sets of trajectories
can be found at https://github.com/Agarciafernande
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
Trajectory PHD and CPHD filters
This paper presents the probability hypothesis density filter (PHD) and the
cardinality PHD (CPHD) filter for sets of trajectories, which are referred to
as the trajectory PHD (TPHD) and trajectory CPHD (TCPHD) filters. Contrary to
the PHD/CPHD filters, the TPHD/TCPHD filters are able to produce trajectory
estimates from first principles. The TPHD filter is derived by recursively
obtaining the best Poisson multitrajectory density approximation to the
posterior density over the alive trajectories by minimising the
Kullback-Leibler divergence. The TCPHD is derived in the same way but
propagating an independent identically distributed (IID) cluster
multitrajectory density approximation. We also propose the Gaussian mixture
implementations of the TPHD and TCPHD recursions, the Gaussian mixture TPHD
(GMTPHD) and the Gaussian mixture TCPHD (GMTCPHD), and the L-scan
computationally efficient implementations, which only update the density of the
trajectory states of the last L time steps.Comment: MATLAB implementations are provided here:
https://github.com/Agarciafernandez/MT
Multiple Object Tracking: A Literature Review
Multiple Object Tracking (MOT) is an important computer vision problem which
has gained increasing attention due to its academic and commercial potential.
Although different kinds of approaches have been proposed to tackle this
problem, it still remains challenging due to factors like abrupt appearance
changes and severe object occlusions. In this work, we contribute the first
comprehensive and most recent review on this problem. We inspect the recent
advances in various aspects and propose some interesting directions for future
research. To the best of our knowledge, there has not been any extensive review
on this topic in the community. We endeavor to provide a thorough review on the
development of this problem in recent decades. The main contributions of this
review are fourfold: 1) Key aspects in a multiple object tracking system,
including formulation, categorization, key principles, evaluation of an MOT are
discussed. 2) Instead of enumerating individual works, we discuss existing
approaches according to various aspects, in each of which methods are divided
into different groups and each group is discussed in detail for the principles,
advances and drawbacks. 3) We examine experiments of existing publications and
summarize results on popular datasets to provide quantitative comparisons. We
also point to some interesting discoveries by analyzing these results. 4) We
provide a discussion about issues of MOT research, as well as some interesting
directions which could possibly become potential research effort in the future
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Distributed Estimation with Information-Seeking Control in Agent Network
We introduce a distributed, cooperative framework and method for Bayesian
estimation and control in decentralized agent networks. Our framework combines
joint estimation of time-varying global and local states with
information-seeking control optimizing the behavior of the agents. It is suited
to nonlinear and non-Gaussian problems and, in particular, to location-aware
networks. For cooperative estimation, a combination of belief propagation
message passing and consensus is used. For cooperative control, the negative
posterior joint entropy of all states is maximized via a gradient ascent. The
estimation layer provides the control layer with probabilistic information in
the form of sample representations of probability distributions. Simulation
results demonstrate intelligent behavior of the agents and excellent estimation
performance for a simultaneous self-localization and target tracking problem.
In a cooperative localization scenario with only one anchor, mobile agents can
localize themselves after a short time with an accuracy that is higher than the
accuracy of the performed distance measurements.Comment: 17 pages, 10 figure
A Multi-Scan Labeled Random Finite Set Model for Multi-object State Estimation
State space models in which the system state is a finite set--called the
multi-object state--have generated considerable interest in recent years.
Smoothing for state space models provides better estimation performance than
filtering by using the full posterior rather than the filtering density. In
multi-object state estimation, the Bayes multi-object filtering recursion
admits an analytic solution known as the Generalized Labeled Multi-Bernoulli
(GLMB) filter. In this work, we extend the analytic GLMB recursion to propagate
the multi-object posterior. We also propose an implementation of this so-called
multi-scan GLMB posterior recursion using a similar approach to the GLMB filter
implementation
Consensus Labeled Random Finite Set Filtering for Distributed Multi-Object Tracking
This paper addresses distributed multi-object tracking over a network of
heterogeneous and geographically dispersed nodes with sensing, communication
and processing capabilities. The main contribution is an approach to
distributed multi-object estimation based on labeled Random Finite Sets (RFSs)
and dynamic Bayesian inference, which enables the development of two novel
consensus tracking filters, namely a Consensus Marginalized
-Generalized Labeled Multi-Bernoulli and Consensus Labeled
Multi-Bernoulli tracking filter. The proposed algorithms provide fully
distributed, scalable and computationally efficient solutions for multi-object
tracking. Simulation experiments via Gaussian mixture implementations confirm
the effectiveness of the proposed approach on challenging scenarios
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