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Vehicle Detection Based on Probability Hypothesis Density Filter. [PDF]
Sensors (Basel), 2016 In the past decade, the developments of vehicle detection have been significantly improved. By utilizing cameras, vehicles can be detected in the Regions of Interest (ROI) in complex environments. However, vision techniques often suffer from false positives and limited field of view.
Zhang F, Knoll A.
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The Gaussian Mixture Probability Hypothesis Density Filter
IEEE Transactions on Signal Processing, 2006 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wing-Kin Ma
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Particle Probability Hypothesis Density Filter Based on Pairwise Markov Chains [PDF]
Algorithms, 2019 Most multi-target tracking filters assume that one target and its observation follow a Hidden Markov Chain (HMC) model, but the implicit independence assumption of the HMC model is invalid in many practical applications, and a Pairwise Markov Chain (PMC) model is more universally suitable than the traditional HMC model.
Jiangyi Liu +3 more
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Trajectory probability hypothesis density filter [PDF]
2018 21st International Conference on Information Fusion (FUSION), 2018 Published in the Proceedings of the 21st International Conference on Information Fusion (FUSION)
Ángel F. García-Fernández +1 more
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Box-particle probability hypothesis density filtering [PDF]
IEEE Transactions on Aerospace and Electronic Systems, 2014 This paper develops a novel approach for multitarget tracking, called box-particle probability hypothesis density filter (box-PHD filter). The approach is able to track multiple targets and estimates the unknown number of targets. Furthermore, it is capable of dealing with three sources of uncertainty: stochastic, set-theoretic, and data association ...
Marek Schikora +4 more
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A general cardinalized probability hypothesis density filter
EURASIP Journal on Advances in Signal Processing, 2022 AbstractBased on random finite set, the probability hypothesis density (PHD) filter and the cardinalized PHD (CPHD) filter have been proposed for multitarget tracking as they are computational tractable. But the classical PHD and CPHD filter is not applicable when a target generates multiple detections.
Xinglin Shen +3 more
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Computation-distributed probability hypothesis density filter [PDF]
EURASIP Journal on Advances in Signal Processing, 2016 Particle probability hypothesis density filtering has become a promising approach for multi-target tracking due to its capability of handling an unknown and time-varying number of targets in a nonlinear, non-Gaussian system. However, its computational complexity linearly increases with the number of obtained observations and the number of particles ...
Junjie Wang 0005 +4 more
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A shrinkage probability hypothesis density filter for multitarget tracking [PDF]
EURASIP Journal on Advances in Signal Processing, 2011 22 ...
Huisi Tong +3 more
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Multisensor Vehicle Tracking with the Probability Hypothesis Density Filter [PDF]
2006 9th International Conference on Information Fusion, 2006 In this contribution we apply the probability hypothesis density (PHD) filter algorithm for joint tracking of an unknown varying number of targets to automotive environment sensing systems. We use data from a vision and a lidar sensor as well as the vehicle ESP system.
Mirko Mählisch +3 more
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State Estimation and Smoothing for the Probability Hypothesis Density Filter [PDF]
, 2021 <p>Tracking multiple objects is a challenging problem for an automated system, with applications in many domains. Typically the system must be able to represent the posterior distribution of the state of the targets, using a recursive algorithm that takes information from noisy measurements.
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