Tracking of crossing targets with imaging sensors

Shertukde, H.M.; Bar‐Shalom, Yaakov

doi:10.1109/7.85031

Cited by 21 publications

(11 citation statements)

References 13 publications

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“…As discussed there, JMPD can be traced back to the event-averaged maximum likelihood estimation (EAMLE) work of Kastella [31,32] and many earlier works, e.g. [28,47,56]. Although this paper is more simply viewed as an application of the JMPD method developed in [31]- [33], it can also be viewed as an application of the FISST method in the setting of [49].…”

Section: Introductionmentioning

confidence: 99%

Multitarget tracking using the joint multitarget probability density

Kreucher¹,

Kastella²,

Hero

2005

IEEE Trans. Aerosp. Electron. Syst.

178

154

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Section: Introductionmentioning

confidence: 99%

Multitarget tracking using the joint multitarget probability density

Kreucher¹,

Kastella²,

Hero

2005

IEEE Trans. Aerosp. Electron. Syst.

178

154

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“…Using the above diagram as a guide, the event probabil for two tracks, 'competing' for any number of common peak generated using the following (approximate) expressions: N2 j2 =-1 N2 j2=-1 A,,(ii ) =A Al(il EI fl'il ) -il =-I lities, s, are (7) (8) (9) In the expressions above, subscripts 1 and 2 denote the lIt and 2nd tracks respectively, j is the event index, N is the number of peaks associated by a given track, , is the PDA event probability, /3' and /3" are the un-normalized and normalized pairwise JPDA event probabilities (respectively), and 6 is the kronecker delta function. The event probabilities of the 2nd track are computed by exchanging the subscripts.…”

Section: I-1198mentioning

confidence: 99%

“…when the track is deemed to be on a target, not clutter, and is presented to the operator). Approaches such as those described in [5][6][7][8][9][10], occupy the middle ground. In these 'hybrid' methods, local clutter statistics and detection characteristics (such as signal-to-noise ratio, peak shape and peak curvature) are generated by the detector and used by the tracker.…”

Section: Introductionmentioning

confidence: 98%

“…When operating in the visible spectrum, peaks are typically identified using statistical change detectors [11,12] to suppress clutter (on background features of no interest). Trackers based on algorithms such as Probabilistic Data Association (PDA) [5][6][7][8]13], Joint Probabilistic Data Association (JPDA) [9,14], or Multiple Hypothesis Tracking (MHT) [10,15,16], are then applied.…”

Section: Introductionmentioning

confidence: 99%

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Detecting and Tracking Moving Objects in Sequences of Color Images

Kennedy¹

2007

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07

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A statistical change detector, implemented as a zero-latency finitememory filter, is used to identify anomalies in temporal pixel statistics. An F-distributed test statistic is computed for each pixel and used in a hypothesis test. The tracker, with automatic track initiation and termination, uses a low-complexity pairwise Joint Probabilistic Data Association (JPDA) algorithm, which has been restricted to consider clusters (sub-problems) containing no more than two tracks. The track state and clutter model are augmented to include color. The detector and tracker are used to process sample video data.

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“…While developed independently [10], [8], the JMPD method can be derived using the mathematics of random sets and expressed in the finite-set statistics framework [9]. As discussed in [11], JMPD can be traced back to the event-averaged maximum-likelihood estimation (EAMLE) work of Kastella [8], [10] and many earlier works, e.g., [12]- [14].…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Approach to Multiple Target Detection and Tracking

Morelande

Kreucher

Kastella

2007

IEEE Trans. Signal Process.

138

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Abstract-This paper considers the problem of simultaneously detecting and tracking multiple targets. The problem can be formulated in a Bayesian framework and solved, in principle, by computation of the joint multitarget probability density (JMPD). In practice, exact computation of the JMPD is impossible, and the predominant challenge is to arrive at a computationally tractable approximation. A particle filtering scheme is developed for this purpose in which each particle is a hypothesis on the number of targets present and the states of those targets. The importance density for the particle filter is designed in such a way that the measurements can guide sampling of both the target number and the target states. Simulation results, with measurements generated from real target trajectories, demonstrate the ability of the proposed procedure to simultaneously detect and track ten targets with a reasonable sample size.

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Tracking of crossing targets with imaging sensors

Cited by 21 publications

References 13 publications

Multitarget tracking using the joint multitarget probability density

Multitarget tracking using the joint multitarget probability density

Detecting and Tracking Moving Objects in Sequences of Color Images

A Bayesian Approach to Multiple Target Detection and Tracking

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