Multitarget tracking using the joint multitarget probability density

Kreucher, Chris; Kastella, Keith; Hero, Alfred O.

doi:10.1109/taes.2005.1561892

Cited by 178 publications

(156 citation statements)

References 51 publications

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“…The JMPD method can also be derived using the mathematics of RFS and expressed in the FISST framework [106]. In [191] and [196], the JMPD is found by the usual Bayesian filtering recursion involving evaluation of the Chapman-Kolmogorov equation followed by the multiplication of the result by the likelihood. This simplifies the calculation of the posterior in comparison with the RFS framework mainly due to the use of vector integrals rather than set integrals.…”

Section: Bayesian Approachesmentioning

confidence: 99%

“…Early work used a deterministic grid approximation, which is practical only for simple problems involving a small number of targets moving in one dimension [197]. PFs have also been used to approximate the JMPD in realistic scenarios involving tracking multitarget [196,198]. PFs provide a recursive stochastic grid approximation to the exact solution of Bayesian state estimation problems.…”

Section: Bayesian Approachesmentioning

confidence: 99%

“…PFs provide a recursive stochastic grid approximation to the exact solution of Bayesian state estimation problems. Based on [191,196] considers targets of varying number. A model for the time evolution of the extended multitarget state is developed that allows the rates at which targets arrive and depart to vary both spatially and temporally.…”

Section: Bayesian Approachesmentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Motion-Based Multitarget Tracking Methods

Qiu¹,

Zhang²,

Lü³

et al. 2015

PIER B

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Abstract-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. A good many methods have been proposed to cope with these challenges. However, no up-to-date survey is available in the literature that can help to select suitable tracking algorithm for practical problem. This paper provides a comprehensive review of the state-of-the-art motion-based MTT techniques, classifies existing methods into two groups, i.e., the detect-beforetrack (DBT) scheme and the track-before-detect (TBD) scheme. The DBT scheme is employed to achieve robust and tractable tracking performance when the signal-noise-ratio (SNR) is strong. The TBD scheme is used in the scenarios of low SNR, and it aims to cumulate target energy by multiple sensor frames. Furthermore, depending on the data association mechanism, the DBT methods can be classified into two categories, data association based approaches and finite set statistics (FISST) based approaches. And the TBD methods can be classified into non-Bayesian approaches and Bayesian approaches depending on the basis theory used for tracking. For each category, this paper provides the detailed descriptions of the representative algorithms, and examines their pros and cons. Finally, new trends for future research directions are offered.

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Section: Bayesian Approachesmentioning

confidence: 99%

Section: Bayesian Approachesmentioning

confidence: 99%

Section: Bayesian Approachesmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Motion-Based Multitarget Tracking Methods

Qiu¹,

Zhang²,

Lü³

et al. 2015

PIER B

View full text Add to dashboard Cite

show abstract

“…Also a colour based particle filter has been combined with an Adaboost classifier to improve the tracking of ice hockey players and to deal with the movement of the camera and the frequent appearance and disappearance of players [15]. Another improvement to the particle filter is called the Adaptive Partition scheme [16]. This scheme adds a bias to the particle importance weighting and allows the particle filter to utilise current measurements during particle prediction.…”

Section: Background On Particle Filtersmentioning

confidence: 99%

Application of the Particle Filter to Tracking of Fish in Aquaculture Research

Pinkiewicz

Williams

Purser

2008

2008 Digital Image Computing: Techniques and Applications

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“…in [8]. Resource allocation for multi-target tracking is also considered in [9] and [10]. In this work, though, we focus on the continuous allocation of energy.…”

Section: Introductionmentioning

confidence: 99%

Adaptive search for sparse targets with informative priors

Newstadt

Bashan

Hero

2010

2010 IEEE International Conference on Acoustics, Speech and Signal Processing

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This works considers the problem of efficient energy allocation of resources in a continuous fashion in determining the location of targets in a sparse environment. We extend the work of Bashan [1] to analyze the use of non-uniform prior knowledge for the location of targets. We show that in the best-case scenario (i.e., when the known prior knowledge is also the underlying prior), then we can get significant gains (several dB) by using a two-level piecewise uniform prior over using the uniform prior that is assumed in [1]. Moreover, even when we have uncertainty in our prior knowledge, we show that we can always do at least as well as the uniform alternative in terms of worst-case and expected gains. In future work, we plan to extend our analysis to general piecewise uniform priors in order to develop multistage (i.e., greater than 2) adaptive energy allocation policies. II. INTRODUCTIONIn many situations, it might be desirable to allocate a limited amount of energy to a small region of interest (ROI) within a larger environment by using adaptive sampling techniques. For example, consider the problem of minimizing communication costs when tracking a target in a distributed sensor network.Clearly, when a node in our sensor network is far from our previous estimate of the target, we would like to reduce the communication from that node in order to preserve its battery life. On the other hand, we would like all sensors within a region near the previous target estimate to be used to estimate the target's position at the next time step. For this purpose, then, we can use adaptive sampling to provide both smart sensor management and improved tracking performance.In another application, we may be interested in locating and estimating the content of tumors in early cancer detection. In this situation, the ROI consists of the tumor location, which in early cancer detection is assumed to be much smaller in area than the entire image. Moreover, the total amount of energy used

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Multitarget tracking using the joint multitarget probability density

Cited by 178 publications

References 51 publications

A Survey of Motion-Based Multitarget Tracking Methods

A Survey of Motion-Based Multitarget Tracking Methods

Application of the Particle Filter to Tracking of Fish in Aquaculture Research

Adaptive search for sparse targets with informative priors

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