Results 11 to 20 of about 785,259 (279)
Pedestrian Group Tracking Using The Gm-Phd Filter
, 2013 CUAS
Edman, Viktor +3 more
core +7 more sources
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
doaj +2 more sources
Remote Sensing, 2022 The Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter can effectively track multiple targets in a single scenario. However, for GM-PHD, unknown target behavior, e.g., target birth or target intersection, produces difficulties in terms of ...
Chao Zhang +4 more
doaj +2 more sources
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 +2 more sources
RCS–Doppler-Assisted MM-GM-PHD Filter for Passive Radar in Non-Uniform Clutter [PDF]
SensorsIn passive radar, the multiple model probability hypothesis density (MM-PHD) filter has demonstrated robust capability in tracking multi-maneuvering targets.
Jia Wang +3 more
doaj +2 more sources
Cooperative Localization for Multi-AUVs Based on GM-PHD Filters and Information Entropy Theory [PDF]
Sensors, 2017 Cooperative localization (CL) is considered a promising method for underwater localization with respect to multiple autonomous underwater vehicles (multi-AUVs).
Lichuan Zhang +3 more
doaj +2 more sources
Enhanced GM-PHD Filter for Real Time Satellite Multi-Target Tracking
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023 We present a real-time multi-object tracker using an enhanced version of the Gaussian mixture probability hypothesis density (GM-PHD) filter to track detections of a state-of-the-art convolutional neural network (CNN). This approach adapts the GM-PHD filter to a real-world scenario to recover target trajectories in remote sensing videos.
Aguilar, Camilo +2 more
core +4 more sources
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
doaj +1 more source
Interactive Model Fusion-Based GM-PHD Filter
CoRR, 2023 In multi-target tracking (MTT), non-Gaussian measurement noise from sensors can diminish the performance of the Gaussian-assumed Gaussian mixture probability hypothesis density (GM-PHD) filter. In this paper, an approach that transforms the MTT problem under non-Gaussian conditions into an MTT problem under Gaussian conditions is developed ...
Jiacheng He +4 more
openaire +2 more sources
Tracking Multiple Targets Using Bearing-Only Measurements in Underwater Noisy Environments
Sensors, 2022 This article handles tracking multiple targets using bearing-only measurements in underwater noisy environments. For tracking multiple targets in underwater noisy environments, the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter provides ...
Jonghoek Kim
doaj +1 more source