Results 21 to 30 of about 3,989 (204)

Label GM-PHD Filter Based on Threshold Separation Clustering

Sensors, 2021
Gaussian mixture probability hypothesis density (GM-PHD) filtering based on random finite set (RFS) is an effective method to deal with multi-target tracking (MTT).
Kuiwu Wang, Qin Zhang, Xiaolong Hu
doaj   +1 more source

Improved Bearings-Only Multi-Target Tracking with GM-PHD Filtering

Sensors, 2016
In this paper, an improved nonlinear Gaussian mixture probability hypothesis density (GM-PHD) filter is proposed to address bearings-only measurements in multi-target tracking.
Qian Zhang, Taek Lyul Song
doaj   +1 more source

A probabilistic hypothesis density filter for traffic flow estimation in the presence of clutter [PDF]

, 2012
Prediction of traffic flow variables such as traffic volume, travel speed or travel time for a short time horizon is of paramount importance in traffic control.
Romain Billot, Mihaylova, Lyudmila, Nour-Eddin El Faouzi, Matthieu Canaud, Sau, Jacques, Jacques Sau, Lyudmila Mihaylova, Canaud, Matthieu, El Faouzi, Nour-Eddin, Billot, Romain   +9 more
core   +1 more source

Probability hypothesis density filtering for real-time traffic state estimation and prediction [PDF]

, 2013
The probability hypothesis density (PHD) methodology is widely used by the research community for the purposes of multiple object tracking. This problem consists in the recursive state estimation of several targets by using the information coming from an
El Faouzi, Nour Eddin, Mihaylova, Lyudmila, Canaud, M., Faouzi, Nour-Eddin El, Sau, Jacques, Canaud, Matthieu, El Faouzi, Nour-Eddin, Mihaylova, L., Sau, J., El Faouzi, N.E.   +9 more
core   +1 more source

Track-Before-Detect Algorithm Based on Improved Auxiliary Particle PHD Filter under Clutter Background

Leida xuebao, 2019
Under the clutter background condition, the existing particle filter pre-detection tracking algorithm based on Probability Hypothesis Density (PHD) filtering is not accurate enough to estimate the number of targets in dense multi-objectives.
PEI Jiazheng, HUANG Yong, DONG Yunlong, HE You, CHEN Xiaolong   +4 more
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Trajectory probability hypothesis density filter [PDF]

, 2018
This paper presents the probability hypothesis density (PHD) filter for sets of trajectories: the trajectory probability density (TPHD) filter. The TPHD filter is capable of estimating trajectories in a principled way without requiring to evaluate all ...
García-Fernández, ÁF, Svensson, L
core   +1 more source

Particle Probability Hypothesis Density Filter Based on Pairwise Markov Chains

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)
Jiangyi Liu, Chunping Wang, Wei Wang, Zheng Li   +3 more
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Convergence of the SMC implementation of the PHD filter [PDF]

, 2005
The probability hypothesis density (PHD) filter is a first moment approximation to the evolution of a dynamic point process which can be used to approximate the optimal filtering equations of the multiple-object tracking problem.
Singh, Sumeetpal S. (Sumeetpal Sidhu), Vo, Ba-Ngu, Johansen, Adam M., Singh, SS, Doucet, Arnaud, Doucet, A, Vo, B, Vo, B-N, ,, Johansen, AM   +9 more
core   +1 more source

Multi-target Tracking Method Based on GM-PHD Filtering with Weight Constraint [PDF]

Jisuanji gongcheng, 2017
Concerning that the Gaussian Mixture Probability Hypothesis Density(GM-PHD) filter does not check one-to-one assumption and it is difficult to track crossing targets,an improved multi-target tracking method with weight constraint is proposed based on GM ...
ZHAO Yifeng
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Multiple Object Tracking Based on Background Subtraction Detection and Improved GM-PHD Filter [PDF]

Jisuanji gongcheng, 2017
Target label confusion and loss are usually caused by occlusion and detection missing in multiple object tracking process,which leads to failing tracking.Aiming at this problem,an improved tracking method based on Gaussian Mixture Probability Hypothesis ...
CHEN Xiangqian,MA Shaohui,XU Wenbo
doaj   +1 more source