A particle filtering approach to constrained motion estimation in tracking multiple targets

I. Kyriakides; D. Morrell; A. Papandreou-Suppappola

A particle filtering approach to constrained motion estimation in tracking multiple targets

I. Kyriakides, D. Morrell, A. Papandreou-Suppappola

Department of Engineering

Arizona State University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Particle filtering has been successfully used in complex target tracking applications such as multiple target tracking. The particle filter can be used to incorporate constraints on target motion to improve tracking performance; this can be achieved using likelihood functions and sampling distributions. In this paper, we propose the constraint likelihood function independent partitions (CLIP) algorithm that uses constraints on target motion. This is achieved by incorporating a constraint likelihood function with the particle weights. As demonstrated by our simulations, a higher increase in tracking performance is obtained with our proposed constrained motion proposal (COMP) algorithm that incorporates target kinematic constraint information directly into the proposal density of the particle filter.

Original language	English
Title of host publication	Conference Record of The Thirty-Ninth Asilomar Conference on Signals, Systems and Computers
Pages	94-98
Number of pages	5
Volume	2005
Publication status	Published - 2005
Event	39th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States Duration: 28 Oct 2005 → 1 Nov 2005

Other

Other	39th Asilomar Conference on Signals, Systems and Computers
Country/Territory	United States
City	Pacific Grove, CA
Period	28/10/05 → 1/11/05

Cite this

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title = "A particle filtering approach to constrained motion estimation in tracking multiple targets",

abstract = "Particle filtering has been successfully used in complex target tracking applications such as multiple target tracking. The particle filter can be used to incorporate constraints on target motion to improve tracking performance; this can be achieved using likelihood functions and sampling distributions. In this paper, we propose the constraint likelihood function independent partitions (CLIP) algorithm that uses constraints on target motion. This is achieved by incorporating a constraint likelihood function with the particle weights. As demonstrated by our simulations, a higher increase in tracking performance is obtained with our proposed constrained motion proposal (COMP) algorithm that incorporates target kinematic constraint information directly into the proposal density of the particle filter.",

author = "I. Kyriakides and D. Morrell and A. Papandreou-Suppappola",

year = "2005",

language = "English",

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volume = "2005",

pages = "94--98",

booktitle = "Conference Record of The Thirty-Ninth Asilomar Conference on Signals, Systems and Computers",

note = "39th Asilomar Conference on Signals, Systems and Computers ; Conference date: 28-10-2005 Through 01-11-2005",

}

Kyriakides, I, Morrell, D & Papandreou-Suppappola, A 2005, A particle filtering approach to constrained motion estimation in tracking multiple targets. in Conference Record of The Thirty-Ninth Asilomar Conference on Signals, Systems and Computers. vol. 2005, 1599709, pp. 94-98, 39th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, United States, 28/10/05.

A particle filtering approach to constrained motion estimation in tracking multiple targets. / Kyriakides, I.; Morrell, D.; Papandreou-Suppappola, A.
Conference Record of The Thirty-Ninth Asilomar Conference on Signals, Systems and Computers. Vol. 2005 2005. p. 94-98 1599709.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Kyriakides, I.

AU - Morrell, D.

AU - Papandreou-Suppappola, A.

PY - 2005

Y1 - 2005

N2 - Particle filtering has been successfully used in complex target tracking applications such as multiple target tracking. The particle filter can be used to incorporate constraints on target motion to improve tracking performance; this can be achieved using likelihood functions and sampling distributions. In this paper, we propose the constraint likelihood function independent partitions (CLIP) algorithm that uses constraints on target motion. This is achieved by incorporating a constraint likelihood function with the particle weights. As demonstrated by our simulations, a higher increase in tracking performance is obtained with our proposed constrained motion proposal (COMP) algorithm that incorporates target kinematic constraint information directly into the proposal density of the particle filter.

AB - Particle filtering has been successfully used in complex target tracking applications such as multiple target tracking. The particle filter can be used to incorporate constraints on target motion to improve tracking performance; this can be achieved using likelihood functions and sampling distributions. In this paper, we propose the constraint likelihood function independent partitions (CLIP) algorithm that uses constraints on target motion. This is achieved by incorporating a constraint likelihood function with the particle weights. As demonstrated by our simulations, a higher increase in tracking performance is obtained with our proposed constrained motion proposal (COMP) algorithm that incorporates target kinematic constraint information directly into the proposal density of the particle filter.

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M3 - Conference contribution

AN - SCOPUS:33847622602

SN - 1424401313

SN - 9781424401314

VL - 2005

SP - 94

EP - 98

BT - Conference Record of The Thirty-Ninth Asilomar Conference on Signals, Systems and Computers

T2 - 39th Asilomar Conference on Signals, Systems and Computers

Y2 - 28 October 2005 through 1 November 2005

ER -

A particle filtering approach to constrained motion estimation in tracking multiple targets

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