Results 11 to 20 of about 4,661,211 (242)
Vehicle Detection Based on Probability Hypothesis Density Filter
Sensors, 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.
Feihu Zhang, Alois Knoll
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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
Mihaylova, Lyudmila +9 more
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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 +9 more
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Applied Sciences, 2017 Underwater multi-targets tracking has always been a difficult problem in active sonar tracking systems. In order to estimate the parameters of time-varying multi-targets moving in underwater environments, based on the Bayesian filtering framework, the ...
Xiao Chen, Yaan Li, Yuxing Li, Jing Yu
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IEEE Access, 2020 The key challenge for multiple vessel cooperative localization is considered as data association, in which state-of-the-art approaches adopt a divide-and-conquer strategy to acquire measurement-to-target association.
Feihu Zhang +3 more
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Robust adaptive multi‐target tracking with unknown measurement and process noise covariance matrices
IET Radar, Sonar & Navigation, 2022 A robust adaptive probability hypothesis density (PHD) filter is proposed to address the degradation of PHD performance due to an unknown process noise and measurement noise covariance matrix.
Peng Gu, Zhongliang Jing, Liangbin Wu
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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
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A Labeled GM-PHD Filter for Explicitly Tracking Multiple Targets
Sensors, 2021 In this study, an explicit track continuity algorithm is proposed for multitarget tracking (MTT) based on the Gaussian mixture (GM) implementation of the probability hypothesis density (PHD) filter. Trajectory maintenance and multitarget state extraction
Yiyue Gao +3 more
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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 +3 more
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Extended Object Tracking Performance Comparison for Autonomous Driving Applications
Engineering Proceedings, 2023 Extended object tracking is crucial for autonomous driving, as it enables vehicles to perceive and respond to their environment accurately by considering an object’s shape, size, and motion over time.
Tolga Bodrumlu +2 more
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