Accelerating Particle filter using multiscale methods

Yaniv Shmueli; Gil Shabat; Amit Bermanis; Amir Averbuch

doi:10.1109/EEEI.2012.6377009

Accelerating Particle filter using multiscale methods

Yaniv Shmueli^*, Gil Shabat, Amit Bermanis, Amir Averbuch

^*Corresponding author for this work

School of Computer Science

Tel Aviv University

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

Abstract

We present a method that accelerates the Particle Filter computation. Particle Filter is a powerful method for tracking the state of a target based on non-linear observations. Unlike the conventional way of calculating weights over all particles in each run, we sample a small subset of the particles using matrix decomposition methods, followed by a novel function extension algorithm to recover the density function of all particles. This significantly reduces the computational load where the measurement computation is substantial, as often happens, for example, when tracking targets in videos. We demonstrate our method on both simulated data and real data (videos).

Original language	English
Title of host publication	2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012
DOIs	https://doi.org/10.1109/EEEI.2012.6377009
State	Published - 2012
Event	2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012 - Eilat, Israel Duration: 14 Nov 2012 → 17 Nov 2012

Publication series

Name	2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012

Conference

Conference	2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012
Country/Territory	Israel
City	Eilat
Period	14/11/12 → 17/11/12

Access to Document

10.1109/EEEI.2012.6377009

Cite this

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title = "Accelerating Particle filter using multiscale methods",

abstract = "We present a method that accelerates the Particle Filter computation. Particle Filter is a powerful method for tracking the state of a target based on non-linear observations. Unlike the conventional way of calculating weights over all particles in each run, we sample a small subset of the particles using matrix decomposition methods, followed by a novel function extension algorithm to recover the density function of all particles. This significantly reduces the computational load where the measurement computation is substantial, as often happens, for example, when tracking targets in videos. We demonstrate our method on both simulated data and real data (videos).",

author = "Yaniv Shmueli and Gil Shabat and Amit Bermanis and Amir Averbuch",

year = "2012",

doi = "10.1109/EEEI.2012.6377009",

language = "אנגלית",

isbn = "9781467346801",

series = "2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012",

booktitle = "2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012",

note = "2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012 ; Conference date: 14-11-2012 Through 17-11-2012",

}

Shmueli, Y, Shabat, G, Bermanis, A & Averbuch, A 2012, Accelerating Particle filter using multiscale methods. in 2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012., 6377009, 2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012, 2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012, Eilat, Israel, 14/11/12. https://doi.org/10.1109/EEEI.2012.6377009

Accelerating Particle filter using multiscale methods. / Shmueli, Yaniv; Shabat, Gil; Bermanis, Amit et al.
2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012. 2012. 6377009 (2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012).

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

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Accelerating Particle filter using multiscale methods

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