Improved Gaussian Mixture Models for Adaptive Foreground Segmentation

Nikolaos Katsarakis, Aristodemos Pnevmatikakis, Zheng-Hua Tan, Ramjee Prasad

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

8 Citationer (Scopus)

Resumé

Adaptive foreground segmentation is traditionally performed using Stauffer & Grimson’s algorithm that models every pixel of the frame by a mixture of Gaussian distributions with continuously adapted parameters. In this paper we provide an enhancement of the algorithm by adding two important dynamic elements to the baseline algorithm: The learning rate can change across space and time, while the Gaussian distributions can be merged together if they become similar due to their adaptation process. We quantify the importance of our enhancements and the effect of parameter tuning using an annotated outdoors sequence.
OriginalsprogEngelsk
TidsskriftWireless Personal Communications
Vol/bind87
Udgave nummer3
Sider (fra-til)629-643
Antal sider14
ISSN0929-6212
DOI
StatusUdgivet - apr. 2016

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Gaussian distribution
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Citer dette

Katsarakis, Nikolaos ; Pnevmatikakis, Aristodemos ; Tan, Zheng-Hua ; Prasad, Ramjee. / Improved Gaussian Mixture Models for Adaptive Foreground Segmentation. I: Wireless Personal Communications. 2016 ; Bind 87, Nr. 3. s. 629-643.
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abstract = "Adaptive foreground segmentation is traditionally performed using Stauffer & Grimson’s algorithm that models every pixel of the frame by a mixture of Gaussian distributions with continuously adapted parameters. In this paper we provide an enhancement of the algorithm by adding two important dynamic elements to the baseline algorithm: The learning rate can change across space and time, while the Gaussian distributions can be merged together if they become similar due to their adaptation process. We quantify the importance of our enhancements and the effect of parameter tuning using an annotated outdoors sequence.",
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Improved Gaussian Mixture Models for Adaptive Foreground Segmentation. / Katsarakis, Nikolaos; Pnevmatikakis, Aristodemos; Tan, Zheng-Hua; Prasad, Ramjee.

I: Wireless Personal Communications, Bind 87, Nr. 3, 04.2016, s. 629-643.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Improved Gaussian Mixture Models for Adaptive Foreground Segmentation

AU - Katsarakis, Nikolaos

AU - Pnevmatikakis, Aristodemos

AU - Tan, Zheng-Hua

AU - Prasad, Ramjee

PY - 2016/4

Y1 - 2016/4

N2 - Adaptive foreground segmentation is traditionally performed using Stauffer & Grimson’s algorithm that models every pixel of the frame by a mixture of Gaussian distributions with continuously adapted parameters. In this paper we provide an enhancement of the algorithm by adding two important dynamic elements to the baseline algorithm: The learning rate can change across space and time, while the Gaussian distributions can be merged together if they become similar due to their adaptation process. We quantify the importance of our enhancements and the effect of parameter tuning using an annotated outdoors sequence.

AB - Adaptive foreground segmentation is traditionally performed using Stauffer & Grimson’s algorithm that models every pixel of the frame by a mixture of Gaussian distributions with continuously adapted parameters. In this paper we provide an enhancement of the algorithm by adding two important dynamic elements to the baseline algorithm: The learning rate can change across space and time, while the Gaussian distributions can be merged together if they become similar due to their adaptation process. We quantify the importance of our enhancements and the effect of parameter tuning using an annotated outdoors sequence.

KW - Adaptive foreground segmentation

KW - Adaptive background mixture models

KW - Gaussian Mixture Models

U2 - 10.1007/s11277-015-2628-3

DO - 10.1007/s11277-015-2628-3

M3 - Journal article

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SP - 629

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JO - Wireless Personal Communications

JF - Wireless Personal Communications

SN - 0929-6212

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