Multiple target tracking with constrained motion using particle filtering methods

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

doi:10.1109/CAMAP.2005.1574190

Multiple target tracking with constrained motion using particle filtering methods

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

In this paper, we propose the constrained motion proposal (COMP) algorithm that incorporates target kinematic constraint information into a particle filter to track multiple targets. We represent deterministic or stochastic constraints on target motion as a likelihood function that is incorporated into the particle filter proposal density. Using Monte Carlo simulations, we demonstrate that this approach improves tracking performance while reducing computational cost relative to the independent partition particle filter with and without a constraint likelihood function.

Original language	English
Title of host publication	IEEE CAMSAP 2005 - First International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Pages	85-88
Number of pages	4
Volume	2005
DOIs	https://doi.org/10.1109/CAMAP.2005.1574190
Publication status	Published - 2005
Event	IEEE CAMSAP 2005 - First International Workshop on Computational Advances in Multi-Sensor Adaptive Processing - Puerto Vallarta, Mexico Duration: 13 Dec 2005 → 15 Dec 2005

Other

Other	IEEE CAMSAP 2005 - First International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Country/Territory	Mexico
City	Puerto Vallarta
Period	13/12/05 → 15/12/05

Access to Document

10.1109/CAMAP.2005.1574190

Cite this

@inproceedings{a572d4e71deb4927848171c4abe75518,

title = "Multiple target tracking with constrained motion using particle filtering methods",

abstract = "In this paper, we propose the constrained motion proposal (COMP) algorithm that incorporates target kinematic constraint information into a particle filter to track multiple targets. We represent deterministic or stochastic constraints on target motion as a likelihood function that is incorporated into the particle filter proposal density. Using Monte Carlo simulations, we demonstrate that this approach improves tracking performance while reducing computational cost relative to the independent partition particle filter with and without a constraint likelihood function.",

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

year = "2005",

doi = "10.1109/CAMAP.2005.1574190",

language = "English",

isbn = "0780393236",

volume = "2005",

pages = "85--88",

booktitle = "IEEE CAMSAP 2005 - First International Workshop on Computational Advances in Multi-Sensor Adaptive Processing",

note = "IEEE CAMSAP 2005 - First International Workshop on Computational Advances in Multi-Sensor Adaptive Processing ; Conference date: 13-12-2005 Through 15-12-2005",

}

Kyriakides, I, Morrell, D & Papandreou-Suppappola, A 2005, Multiple target tracking with constrained motion using particle filtering methods. in IEEE CAMSAP 2005 - First International Workshop on Computational Advances in Multi-Sensor Adaptive Processing. vol. 2005, 1574190, pp. 85-88, IEEE CAMSAP 2005 - First International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Puerto Vallarta, Mexico, 13/12/05. https://doi.org/10.1109/CAMAP.2005.1574190

Multiple target tracking with constrained motion using particle filtering methods. / Kyriakides, I.; Morrell, D.; Papandreou-Suppappola, A.
IEEE CAMSAP 2005 - First International Workshop on Computational Advances in Multi-Sensor Adaptive Processing. Vol. 2005 2005. p. 85-88 1574190.

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

TY - GEN

T1 - Multiple target tracking with constrained motion using particle filtering methods

AU - Kyriakides, I.

AU - Morrell, D.

AU - Papandreou-Suppappola, A.

PY - 2005

Y1 - 2005

N2 - In this paper, we propose the constrained motion proposal (COMP) algorithm that incorporates target kinematic constraint information into a particle filter to track multiple targets. We represent deterministic or stochastic constraints on target motion as a likelihood function that is incorporated into the particle filter proposal density. Using Monte Carlo simulations, we demonstrate that this approach improves tracking performance while reducing computational cost relative to the independent partition particle filter with and without a constraint likelihood function.

AB - In this paper, we propose the constrained motion proposal (COMP) algorithm that incorporates target kinematic constraint information into a particle filter to track multiple targets. We represent deterministic or stochastic constraints on target motion as a likelihood function that is incorporated into the particle filter proposal density. Using Monte Carlo simulations, we demonstrate that this approach improves tracking performance while reducing computational cost relative to the independent partition particle filter with and without a constraint likelihood function.

UR - http://www.scopus.com/inward/record.url?scp=33846621490&partnerID=8YFLogxK

U2 - 10.1109/CAMAP.2005.1574190

DO - 10.1109/CAMAP.2005.1574190

M3 - Conference contribution

AN - SCOPUS:33846621490

SN - 0780393236

SN - 9780780393233

VL - 2005

SP - 85

EP - 88

BT - IEEE CAMSAP 2005 - First International Workshop on Computational Advances in Multi-Sensor Adaptive Processing

T2 - IEEE CAMSAP 2005 - First International Workshop on Computational Advances in Multi-Sensor Adaptive Processing

Y2 - 13 December 2005 through 15 December 2005

ER -

Multiple target tracking with constrained motion using particle filtering methods

Abstract

Other

Access to Document

Other files and links

Fingerprint

Cite this