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Spectral multiscale coverage with the feature aided CPHD tracker

Ramona Georgescu, Shuo Zhang, Amit Surana, Alberto Speranzon, Ozgur Erdinc
2014 2014 48th Asilomar Conference on Signals, Systems and Computers  
) multitarget tracker.  ...  A closed loop approach for surveillance was developed leveraging the Spectral Multiscale Coverage (SMC) algorithm for sensor management coupled with the Cardinalized Probability Hypothesis Density (CPHD  ...  Andrzej Banaszuk at United Technologies Research Center for their support on this project.  ... 
doi:10.1109/acssc.2014.7094583 dblp:conf/acssc/GeorgescuZSSE14 fatcat:ubfcs4zh4zhjjl3ndgzcqeylpm

The CPHD and R-RANSAC trackers applied to the VIVID dataset

Ramona Georgescu, Peter Niedfeldt, Shuo Zhang, Amit Surana, Alberto Speranzon, Ozgur Erdinc, Oliver E. Drummond
2014 Signal and Data Processing of Small Targets 2014  
In this work, two multitarget trackers -the Cardinalized Probability Hypothesis Density (CPHD) filter and the Recursive Random Sample Consensus (R-RANSAC) algorithm -were applied to three scenarios of  ...  The same detector output was given to each tracker and the same metrics of performance were computed in order to ensure fair comparison of the two tracking approaches.  ...  Andrzej Banaszuk at United Technologies Research Center for their support on this project.  ... 
doi:10.1117/12.2068075 fatcat:qimvvwkxcrdqbkjqzwmb3xfdqy

Distributed Multitarget Tracking With Range-Doppler Sensors

Giorgio Battistelli, Luigi Chisci, Claudio Fantacci, Alfonso Farina, Antonio Graziano
2013 Zenodo  
To this end, in [1] an efficient distributed consensusbased multitarget multisensor tracker, named CGM-CPHD (Consensus Gaussian-Mixture Cardinalized Probability Hypothesis Density) filter, has been developed  ...  CONSENSUS MULTITARGET TRACKER This section presents the proposed CGM-CPHD filter algorithm [1] .  ... 
doi:10.5281/zenodo.43667 fatcat:so4tbkigabevbpfhv23ck2o24a

AMPLITUDE-AIDED CPHD FILTER FOR MULTITARGET TRACKING IN INFRARED IMAGES

Changzhen Qiu, Zhiyong Zhang, Huanzhang Lu, Yabei Wu
2014 Progress in Electromagnetics Research B  
The cardinalized probability hypothesis density (CPHD) filter is a powerful tool for multitarget tracking (MTT).  ...  In the implementation, we adopt the Gaussian mixture (GM) approach to implement the amplitude-aided CPHD filter to achieve efficient performance.  ...  Also, the GM implementation of the new CPHD tracker is given. Conventional CPHD Recursion Based on the RFS framework, the multitarget tracking can be unified to Bayesian framework rigorously.  ... 
doi:10.2528/pierb14092101 fatcat:bvfjt7rthnfhrkzy4ctfi52qwe

A SURVEY OF MOTION-BASED MULTITARGET TRACKING METHODS

Changzhen Qiu, Zhiyong Zhang, Huanzhang Lu, Huiwu Luo
2015 Progress in Electromagnetics Research B  
Multitarget tracking (MTT) in surveillance system is extremely challenging, due to uncertain data association, maneuverable target motion, dense clutter disturbance, and real-time processing requirements  ...  However, no up-to-date survey is available in the literature that can help to select suitable tracking algorithm for practical problem.  ...  A recursive estimator, denoted as Gaussian mixture KF (GMKF), for linear non-Gaussian problems was derived in [77, 78] .  ... 
doi:10.2528/pierb15010503 fatcat:zvucqzcgdjdy5pnvqg6b4mcnye

The GM-CPHD Tracker Applied to Real and Realistic Multistatic Sonar Data Sets

R. Georgescu, P. Willett
2012 IEEE Journal of Oceanic Engineering  
The Gaussian Mixture-Cardinalized Probability Hypothesis Density (GM-CPHD) tracker was applied to three real and simulated multi-static sonar datasets.  ...  The first two datasets presented minor challenges for the tracker and in our opinion demonstrate that it is, indeed, a tracking paradigm that is ready for "prime time".  ...  The posterior CPHD surface was approximated by a Gaussian Mixture and was shown to remain a Gaussian implementations of the PHD/CPHD filters were also proposed (we mention only [12] [13], more exist)  ... 
doi:10.1109/joe.2012.2186859 fatcat:cbc65habpfbend6pl6cfwm7m74

The Multiple Model CPHD Tracker

Ramona Georgescu, Peter Willett
2012 IEEE Transactions on Signal Processing  
It is implemented using Gaussian mixtures, and a track management (for display and scoring) strategy is developed.  ...  The Probability Hypothesis Density (PHD) is a practical approximation to the full Bayesian multitarget filter.  ...  The posterior CPHD surface was approximated by a Gaussian Mixture and was shown to remain a Gaussian Mixture after the update step.  ... 
doi:10.1109/tsp.2012.2183128 fatcat:pdortt2r4ff4tns4gxfq6or3ci

A Particle Multi-Target Tracker for Superpositional Measurements using Labeled Random Finite Sets [article]

Francesco Papi, Du Yong Kim
2015 arXiv   pre-print
In this paper we present a general solution for multi-target tracking with superpositional measurements.  ...  We propose an efficient multi-target sampling strategy based on Superpositional Approximate CPHD (SA-CPHD) filter and the recently introduced Labeled Multi-Bernoulli (LMB) and Vo-Vo densities.  ...  The CPHD filter can be implemented with Gaussian mixtures or particles [42] , while only the particle implementation is available for the SA-CPHD filter [4] .  ... 
arXiv:1501.02248v2 fatcat:b77x4kgpsnfvzcyzaml7px4ehi

Bayesian multi-target tracking with superpositional measurements using labeled random finite sets

Francesco Papi, Du Yong Kim
2015 2015 23rd European Signal Processing Conference (EUSIPCO)  
In this paper we present a general solution for multi-target tracking problems with superpositional measurements.  ...  implementation of this filter using Sequential Monte Carlo (SMC) methods with an efficient multi-target sampling strategy based on the Approximate Superpositional Cardinalized Probability Hypothesis Density (CPHD  ...  Inspired by [1, 2] , this paper proposes a multi-target tracker for superpositional sensors which estimates target tracks and requires only one level of approximation.  ... 
doi:10.1109/eusipco.2015.7362777 dblp:conf/eusipco/PapiK15 fatcat:lpoq3gma2bcndgekygwngf2rwm

Distributed GM-CPHD filter based on Generalized Inverse Covariance Intersection

Woo Jung Park, Chan Gook Park
2021 IEEE Access  
GAUSSIAN MIXTURE IMPLEMENTATION For Gaussian mixture form of the local density ( ) ( ) ( ) 1( ) ( , ), a G N a a a a j j j j sN     x x P (15) If the Gaussian components are well-separated as     ...  CONCLUSION In this research, we proposed the distributed GM-CPHD filter based on GICI and verified performance with multitarget tracking simulations.  ... 
doi:10.1109/access.2021.3093719 fatcat:qhhav53o65crzppht2nt2dqj7i

Multi-Target Tracking With Time-Varying Clutter Rate and Detection Profile: Application to Time-Lapse Cell Microscopy Sequences

Seyed Hamid Rezatofighi, Stephen Gould, Ba Tuong Vo, Ba-Ngu Vo, Katarina Mele, Richard Hartley
2015 IEEE Transactions on Medical Imaging  
In this paper, we propose a framework for tracking these structures based on the random finite set Bayesian filtering framework.  ...  The estimator adaptively estimates the required meta parameters for the tracker such as clutter rate and the detection probability of the targets, while the tracker estimates the state of the targets.  ...  Anthony Dick from the University of Adelaide whose comments and suggestions improved presentation of the paper.  ... 
doi:10.1109/tmi.2015.2390647 pmid:25594963 fatcat:p6iidcn6encj5j4cjuev6eu63y

Neuromorphic Vision Based Multivehicle Detection and Tracking for Intelligent Transportation System

Guang Chen, Hu Cao, Muhammad Aafaque, Jieneng Chen, Canbo Ye, Florian Röhrbein, Jörg Conradt, Kai Chen, Zhenshan Bing, Xingbo Liu, Gereon Hinz, Walter Stechele (+1 others)
2018 Journal of Advanced Transportation  
We performed a preliminary multivehicle tracking-by-clustering study using three classical clustering approaches and four tracking approaches.  ...  results in advantageous characteristics, in terms of low energy consumption, high dynamic range, sparse event stream, and low response latency, which can be very useful in intelligent perception systems for  ...  In terms of tracking stage, we carry out online multitarget tracking via four different algorithms: simple online and real-time tracking (SORT) [7] , the Gaussian mixture probability hypothesis density  ... 
doi:10.1155/2018/4815383 fatcat:a4jmeozosjg5zpx6jfdwovmvcm

Multisensor RFS Filters for Unknown and Changing Detection Probability

Zhang, Li, Sun
2019 Electronics  
The detection probability is an important parameter in multisensor multitarget tracking.  ...  Therefore, to alleviate the performance deterioration caused by the mismatch of the detection probability, this paper applies the inverse gamma Gaussian mixture (IGGM) distribution to both the MS-MeMBer  ...  For this reason, the inverse gamma Gaussian mixture (IGGM) model is used in CPHD/PHD filters [29] .  ... 
doi:10.3390/electronics8070741 fatcat:wfdy542g5bbszfokuvobr2amc4

Multitarget Tracking Using One Time Step Lagged Delta-generalized Labeled Multi-Bernoulli Smoothing

Guolong Liang, Quanrui Li, Bin Qi, Longhao Qiu
2020 IEEE Access  
In this work, aiming at improving the tracking performance of the -GLMB d filter, namely improving the estimates of target number and state, we present a one time step lagged Bayes multitarget smoother  ...  A forward-backward multi-Bernoulli (MB) smoother for multi-target tracking was given in [30] , in which they state that it improves the estimation accuracy of target number and state over the MB filter  ...  NUMERICAL RESULTS In this section, we show the comparison of the proposed smoother with the PHD [28] , MB [30] , and CPHD [31] smoothers, and then presents its comparison with the LMB [33] , and -  ... 
doi:10.1109/access.2020.2971624 fatcat:4pdwnueurzgqpg7pw7ehpakapu

"Statistics 102" for Multisource-Multitarget Detection and Tracking

Ronald Mahler
2013 IEEE Journal on Selected Topics in Signal Processing  
Finite-set statistics provides powerful new conceptual and computational methods for dealing with multisensor-multitarget detection and tracking problems.  ...  Finally, it describes how multitarget calculus is used to derive principled statistical approximations of this optimal filter, such as PHD filters, CPHD filters, and multi-Bernoulli filters.  ...  The classical PHD and CPHD filters are most commonly implemented using either Gaussian mixture techniques (assuming moderate motion and/or measurement nonlinearities) or particle methods (for stronger  ... 
doi:10.1109/jstsp.2013.2253084 fatcat:6pnapv6s2rdnrdiv62uyizrx7y
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