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Multisensor Multi-Target Tracking Based on GM-PHD Using Out-Of-Sequence Measurements

Meiqin Liu, Tianyi Huai, Ronghao Zheng, Senlin Zhang
2019 Sensors  
In this paper, we study the issue of out-of-sequence measurement (OOSM) in a multi-target scenario to improve tracking performance.  ...  Finally, several simulations are given, and results show that tracking performance of GM-PHD using OOSM is better than GM-PHD using in-sequence measurement (ISM), which can strongly demonstrate the effectiveness  ...  Based on this, we used OOSM to re-update the posterior intensity of GM-PHD.  ... 
doi:10.3390/s19194315 fatcat:tjgwld7a3zc6jh3pwifhtal2yi

Development of a N-type GM-PHD Filter for Multiple Target, Multiple Type Visual Tracking [article]

Nathanael L. Baisa, Andrew Wallace
2019 arXiv   pre-print
For both cases, Munkres's variant of the Hungarian assignment algorithm is used to associate tracked target identities between frames.  ...  This shows the improved performance of our strategy on real video sequences.  ...  Daniel Clark for sharing his expertise and understanding of RFS methodology.  ... 
arXiv:1706.00672v5 fatcat:7gmvzmljqrbqllvosr6e2eyzga

Multi-Level Cooperative Fusion of GM-PHD Filters for Online Multiple Human Tracking

Zeyu Fu, Federico Angelini, Jonathon Chambers, Syed Mohsen Naqvi
2019 IEEE transactions on multimedia  
In this paper, we propose a multi-level cooperative fusion approach to address the online multiple human tracking problem in a Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter framework.  ...  Experiments on the MOTChallenge Benchmark demonstrate the proposed method achieves improved performance over other state-of-the-art RFS based tracking methods.  ...  GM-PHD and Sequential Monte Carlo (SMC)-PHD filters are two commonly used implementations in this theory, as they have been able to generate convincing tracking performance in video-based multi-target  ... 
doi:10.1109/tmm.2019.2902480 fatcat:ildjc6cykrhpthrhhroqqrnlny


Changzhen Qiu, Zhiyong Zhang, Huanzhang Lu, Huiwu Luo
2015 Progress in Electromagnetics Research B  
The TBD scheme is used in the scenarios of low SNR, and it aims to cumulate target energy by multiple sensor frames.  ...  And the TBD methods can be classified into non-Bayesian approaches and Bayesian approaches depending on the basis theory used for tracking.  ...  With the assumption that target state and measurement obey a linear Gaussian model, an effective approach based on GM can be used to implement PHD/CPHD, which denoted as GM-PHD/GM-CPHD.  ... 
doi:10.2528/pierb15010503 fatcat:zvucqzcgdjdy5pnvqg6b4mcnye

Probability hypothesis density filtering with sensor networks and irregular measurement sequences

A N Bishop
2010 2010 13th International Conference on Information Fusion  
The problem of multi-object tracking with sensor networks is studied using the probability hypothesis density filter.  ...  These signals may arrive out-oforder (out-of-sequence), be corrupted or even lost due to, e.g., noise in the communication medium and protocol malfunctions.  ...  See [3, 4, 7] for a comprehensive background on random finite set-based target tracking and the PHD filter.  ... 
doi:10.1109/icif.2010.5711952 fatcat:4v43olrkvnbsfiw4a6beq5tt3q

Cooperative-PHD Tracking Based on Distributed Sensors for Naval Surveillance Area

Kleberson Meireles de Lima, Ramon Romankevicius Costa
2022 Sensors  
The proposed data fusion scheme, utilized in the central station, consists of an additional step of prune & merge to the original GM PHD filter algorithm, which is the main contribution of this work.  ...  This paper focuses on a multitarget tracking application to a large-scale maritime surveillance system. The system is composed of a sensor network distributed over an area of interest.  ...  [50] investigate the use of GM PHD filters for multiple people tracking using a network of radar sensors in an indoor environment.  ... 
doi:10.3390/s22030729 pmid:35161477 pmcid:PMC8838208 fatcat:tpjbjpya2zgivnaxa7biytocqa

Filtering Point Targets via Online Learning of Motion Models [article]

Mehryar Emambakhsh, Alessandro Bay, Eduard Vazquez
2019 arXiv   pre-print
Fixed motion models can fail to provide accurate predictions, while learning based algorithm can be difficult to design (due to the variable number of targets), slow to train and dependent on separate  ...  To address these issues, this paper proposes a multi-target filtering algorithm which learns the motion models, on the fly, using a recurrent neural network with a long short-term memory architecture,  ...  Schindler, “Online measures and a data set for multi-target, multi-camera tracking,” in multi-target tracking using recurrent neural networks,” in AAAI, 2017.  ... 
arXiv:1902.07630v1 fatcat:emcostdvibfnjjg23lbljwkjgm

Data fusion of radar and stereo vision for detection and tracking of moving objects

Frik J. Botha, Corne E. van Daalen, Johann Treurnicht
2016 2016 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech)  
The closed-form GM-PHD formulas laid out in Section 5.2 are therefore directly applicable for the radar-based recursive state estimation.  ...  A sparse motion-based technique coupled with density-based clustering was used to extract measurements from stereo vision image sequences.  ...  sampling, uses a proposal probability distribution that serves to simulate a given target distribution [85] .  ... 
doi:10.1109/robomech.2016.7813156 fatcat:nerj3imhrzftbp6rrvfdivtz2m

A Labeled GM-PHD Filter for Explicitly Tracking Multiple Targets

Yiyue Gao, Defu Jiang, Chao Zhang, Su Guo
2021 Sensors  
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.  ...  This provides the identity label of the individual target and can shield against the negative effects of clutter in the prior density region on the estimates, thus realizing the integration of trajectory  ...  Introduction Background and Motivation Multitarget tracking (MTT) jointly estimates the target state and the number of targets simultaneously from a sequence of measurements [1] .  ... 
doi:10.3390/s21113932 fatcat:kxokmhizr5ccvmmv5qddaotxby

Multiple Target, Multiple Type Filtering in the RFS Framework [article]

Nathanael L. Baisa, Andrew Wallace
2019 arXiv   pre-print
Furthermore, we analyze the results from simulations to track sixteen targets of four different types using a four-type (quad) GM-PHD filter as a typical example and compare it with four independent GM-PHD  ...  Then, under the assumptions of Gaussianity and linearity, we extend the Gaussian mixture (GM) implementation of the standard PHD filter for the proposed N-type PHD filter termed the N-type GM-PHD filter  ...  Traditionally, multi-target filters are based on finding associations between targets and measurements using methods including Global Nearest Neighbour (GNN) [1] [2] , Joint Probabilistic Data Association  ... 
arXiv:1705.04757v6 fatcat:bgxvp7juj5d4zlwt2srt52endi

PHD Filter for Object Tracking in Road Traffic Applications Considering Varying Detectability

Olivér Törő, Tamás Bécsi, Péter Gáspár
2021 Sensors  
The goal is to maintain and report the state of undetected though possibly present objects. The proposed algorithm is based on the multi-object Probability Hypothesis Density filter.  ...  The performance of the algorithm is demonstrated using highway radar measurements.  ...  Gao et al. proposed a multi-frame GM-PHD filter to manage the weights of Gaussian components corresponding to undetected targets.  ... 
doi:10.3390/s21020472 pmid:33440810 pmcid:PMC7826700 fatcat:bjwtkhz3wran7lmyasrlpk2464

Multi-sensor joint target detection, tracking and classification via Bernoulli filter [article]

Gaiyou Li, Ping Wei, Giorgio Battistelli, Luigi Chisci, Lin Gao
2021 arXiv   pre-print
This paper focuses on joint detection, tracking and classification (JDTC) of a target via multi-sensor fusion.  ...  The target can be present or not, can belong to different classes, and depending on its class can behave according to different kinematic modes.  ...  Multi-sensor update of C-JDTC-BF Proposition 2 provides the update equations based on Bayes-optimal C-JDTC-BF.  ... 
arXiv:2109.11259v1 fatcat:jozya2o3x5gwxav6rlbt4qrege

Effective World Modeling: Multisensor Data Fusion Methodology for Automated Driving

Jos Elfring, Rein Appeldoorn, Sjoerd van den Dries, Maurice Kwakkernaat
2016 Sensors  
A one-size-fits-all multisensor data fusion architecture is not realistic due to the enormous diversity in vehicles, sensors and applications.  ...  Abstract: The number of perception sensors on automated vehicles increases due to the increasing number of advanced driver assistance system functions and their increasing complexity.  ...  Conflicts of Interest: The authors declare no conflict of interest. Sensors 2016, 16, 1668  ... 
doi:10.3390/s16101668 pmid:27727171 pmcid:PMC5087456 fatcat:6bmajh7uczfjxos5efweuh7dea

Multisensor data fusion: A review of the state-of-the-art

Bahador Khaleghi, Alaa Khamis, Fakhreddine O. Karray, Saiedeh N. Razavi
2013 Information Fusion  
There has been an ever-increasing interest in multi-disciplinary research on multisensor data fusion technology, driven by its versatility and diverse areas of application.  ...  This paper proposes a comprehensive review of the data fusion state of the art, exploring its conceptualizations, benefits, and challenging aspects, as well as existing methodologies.  ...  In case of multi-target tracking, the first moment is the Probability Hypothesis Density (PHD), which is used to develop a filter with the same title, i.e. PHD filter [86] .  ... 
doi:10.1016/j.inffus.2011.08.001 fatcat:42ca63cpqzea3o2w7wuzwwvy7e

Auxiliary Particle Implementation of Probability Hypothesis Density Filter

Nick Whiteley, Sumeetpal Singh, Simon Godsill
2010 IEEE Transactions on Aerospace and Electronic Systems  
The Probability Hypothesis Density (PHD) filter propagates the first moment of the multi-target posterior distribution.  ...  Optimal Bayesian multi-target filtering is, in general, computationally impractical owing to the high dimensionality of the multi-target state.  ...  to multi-target tracking not based on the PP formalism).  ... 
doi:10.1109/taes.2010.5545199 fatcat:2r4iyl4gcbaepgwl2tsymuwugm
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