Box-Particle Cardinality Balanced Multi-Target Multi-Bernoulli Filter
Radioengineering, 2014 As a generalized particle filtering, the box-particle filter (Box-PF) has a potential to process the measurements affected by bounded error of unknown distributions and biases.
L. Song, X. Zhao
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Trajectory Poisson Multi-Bernoulli Filters [PDF]
IEEE Transactions on Signal Processing, 2020 This paper presents two trajectory Poisson multi-Bernoulli (TPMB) filters for multi-target tracking: one to estimate the set of alive trajectories at each time step and another to estimate the set of all trajectories, which includes alive and dead trajectories, at each time step.
Ángel F. García-Fernández +4 more
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Interaction-Aware Labeled Multi-Bernoulli Filter
IEEE Transactions on Intelligent Transportation Systems, 2023 13 pages including references, 9 figures, submitted and undergoing second round of review with IEEE Transactions on Intelligent Transportation Systems (ITS)
Nida Ishtiaq +3 more
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Bernoulli Particle/Box-Particle Filters for Detection and Tracking in the Presence of Triple Measurement Uncertainty [PDF]
, 2012 This work presents sequential Bayesian detection and estimation methods for nonlinear dynamic stochastic systems using measurements affected by three sources of uncertainty: stochastic, set-theoretic and data association uncertainty.
Gning, Amadou +5 more
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The Fast Product Multi-Sensor Labeled Multi-Bernoulli Filter
, 2023 The multi-sensor Labeled Multi-Bernoulli filter has the challenge of relying on the NP-hard multi-sensor update of the Generalized Labeled Multi-Bernoulli filter.
Herrmann, Martin +4 more
core +1 more source
Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods
Sensors, 2017 We develop an interactive likelihood (ILH) for sequential Monte Carlo (SMC) methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association.
Anthony Hoak +2 more
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Adaptive Multi-target Tracking Algorithm with Unknown Detection Probability [PDF]
Jisuanji gongcheng, 2017 In order to accurately model the system detection probability in a complex background,a Multi-Target Tracking(MTT) method with unknown detection probability is proposed.The detection probability is modeled by the Time Varying AutoRegressive(TVAR) process.
LIU Jun,YUAN Peiyan,QIU Hao
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Distributed Implementation of the Centralized Generalized Labeled Multi-Bernoulli Filter [PDF]
IEEE Transactions on Signal Processing, 2021 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Martin Herrmann +2 more
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Particle filter implementation of the multi-Bernoulli filter for superpositional sensors [PDF]
2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2013 The multi-Bernoulli filter is a promising method for computationally efficient and accurate multi-target tracking. Computationally tractable approximations of the multi-Bernoulli filter equations for superpositional sensors were recently derived. In this paper we present a particle filter implementation of these approximate update filter equations.
Santosh Nannuru, Mark Coates
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Detection Optimized Labeled Multi-Bernoulli Algorithm for Visual Multi-target Tracking [PDF]
Jisuanji kexue yu tansuo, 2023 In a video multi-target tracking algorithm combining a detector with a tracker, the quality of detector affects the performance of the whole tracking algorithm.
JIANG Lingyun, YANG Jinlong
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