1,119 research outputs found
Multitarget tracking with interacting population-based MCMC-PF
In this paper we address the problem of tracking multiple targets based on raw measurements by means of Particle filtering. This strategy leads to a high computational complexity as the number of targets increases, so that an efficient implementation of the tracker is necessary. We propose a new multitarget Particle Filter (PF) that solves such challenging problem. We call our filter Interacting Population-based MCMC-PF (IP-MCMC-PF) since our approach is based on parallel usage of multiple population-based Metropolis-Hastings (M-H) samplers. Furthermore, to improve the chains mixing properties, we exploit genetic alike moves performing interaction between the Markov Chain Monte Carlo (MCMC) chains. Simulation analyses verify a dramatic reduction in terms of computational time for a given track accuracy, and an increased robustness w.r.t. conventional MCMC based PF
Multitarget Tracking with Split and Merged Measurements
©2005 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Presented at the 2005 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 20-25 June 2005, San Diego, CA.DOI: 10.1109/CVPR.2005.245In many multitarget tracking applications in computer
vision, a detection algorithm provides locations of potential
targets. Subsequently, the measurements are associated
with previously estimated target trajectories
in a data association step. The output of the detector
is often imperfect and the detection data may include
multiple, split measurements from a single target
or a single merged measurement from several targets.
To address this problem, we introduce a multiple
hypothesis tracker for interacting targets that generate
split and merged measurements. The tracker is based on
an efficient Markov chain Monte Carlo (MCMC) based
auxiliary variable particle filter. The particle filter is
Rao-Blackwellized such that the continuous target state
parameters are estimated analytically, and an MCMC
sampler generates samples from the large discrete space
of data associations. In addition, we include experimental
results in a scenario where we track several interacting
targets that generate these split and merged measurements
A track-before-detect labelled multi-Bernoulli particle filter with label switching
This paper presents a multitarget tracking particle filter (PF) for general
track-before-detect measurement models. The PF is presented in the random
finite set framework and uses a labelled multi-Bernoulli approximation. We also
present a label switching improvement algorithm based on Markov chain Monte
Carlo that is expected to increase filter performance if targets get in close
proximity for a sufficiently long time. The PF is tested in two challenging
numerical examples.Comment: Accepted for publication in IEEE Transactions on Aerospace and
Electronic System
Behavior Extraction from Examples Using Federate MCMC-Based Particle Filtering
AbstractData-driven methods of simulating a crowd of virtual humans that exhibit behaviors imitating real human crowds play an important role in crowd simulation. In this paper, we propose a Bayesian framework for the extraction of real human's behaviors which exhibit interactions in their daily life using multiple fixed cameras. The described Markov chain Monte Carlo particle filter can effectively deals with interacting targets which are influenced by the proximity and behaviors of other targets. In this paper, we use a Markov random field motion prior combing with a federate filter algorithm which treats the observations discriminatorily to substantially improve the tracking of a fixed number of interacting targets. Simultaneously, we replace the traditional importance sampling step with MCMC sampling step to get over the vast computational requirements for large numbers of targets. i.e., we focus on the data fusion and the behavior recognition process. Finally, experimental results demonstrate that the proposed Bayesian framework deals efficiently and effectively with extractions of interacting behavior
Random finite sets in multi-target tracking - efficient sequential MCMC implementation
Over the last few decades multi-target tracking (MTT) has proved to be a challenging and attractive research topic. MTT applications span a wide variety of disciplines, including robotics, radar/sonar surveillance, computer vision and biomedical research. The primary focus of this dissertation is to develop an effective and efficient multi-target tracking algorithm dealing with an unknown and time-varying number of targets. The emerging and promising Random Finite Set (RFS) framework provides a rigorous foundation for optimal Bayes multi-target tracking. In contrast to traditional approaches, the collection of individual targets is treated as a set-valued state. The intent of this dissertation is two-fold; first to assert that the RFS framework not only is a natural, elegant and rigorous foundation, but also leads to practical, efficient and reliable algorithms for Bayesian multi-target tracking, and second to provide several novel RFS based tracking algorithms suitable for the specific Track-Before-Detect (TBD) surveillance application. One main contribution of this dissertation is a rigorous derivation and practical implementation of a novel algorithm well suited to deal with multi-target tracking problems for a given cardinality. The proposed Interacting Population-based MCMC-PF algorithm makes use of several Metropolis-Hastings samplers running in parallel, which interact through genetic variation. Another key contribution concerns the design and implementation of two novel algorithms to handle a varying number of targets. The first approach exploits Reversible Jumps. The second approach is built upon the concepts of labeled RFSs and multiple cardinality hypotheses. The performance of the proposed algorithms is also demonstrated in practical scenarios, and shown to significantly outperform conventional multi-target PF in terms of track accuracy and consistency. The final contribution seeks to exploit external information to increase the performance of the surveillance system. In multi-target scenarios, kinematic constraints from the interaction of targets with their environment or other targets can restrict target motion. Such motion constraint information is integrated by using a fixed-lag smoothing procedure, named Knowledge-Based Fixed-Lag Smoother (KB-Smoother). The proposed combination IP-MCMC-PF/KB-Smoother yields enhanced tracking
Langevin and Hamiltonian based Sequential MCMC for Efficient Bayesian Filtering in High-dimensional Spaces
Nonlinear non-Gaussian state-space models arise in numerous applications in
statistics and signal processing. In this context, one of the most successful
and popular approximation techniques is the Sequential Monte Carlo (SMC)
algorithm, also known as particle filtering. Nevertheless, this method tends to
be inefficient when applied to high dimensional problems. In this paper, we
focus on another class of sequential inference methods, namely the Sequential
Markov Chain Monte Carlo (SMCMC) techniques, which represent a promising
alternative to SMC methods. After providing a unifying framework for the class
of SMCMC approaches, we propose novel efficient strategies based on the
principle of Langevin diffusion and Hamiltonian dynamics in order to cope with
the increasing number of high-dimensional applications. Simulation results show
that the proposed algorithms achieve significantly better performance compared
to existing algorithms
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