The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Cet article présente la corrélation probabiliste bipolaire de portée radiale (PrBPRRC), une méthode de détection de changement robuste aux changements d'éclairage et aux mouvements d'arrière-plan. La plupart des méthodes traditionnelles de détection des changements sont robustes aux changements d'éclairage ou aux mouvements d'arrière-plan ; BPRRC est l’une des méthodes de détection de changement robustes en éclairage. Nous introduisons un modèle probabiliste de texture d'arrière-plan dans BPRRC et ajoutons la robustesse aux mouvements d'arrière-plan, y compris les invasions de premier plan telles que les voitures en mouvement, les gens qui marchent, les arbres qui se balancent et les chutes de neige. Nous montrons la supériorité du PrBPRRC dans l'environnement avec les changements d'éclairage et les mouvements d'arrière-plan en utilisant trois ensembles de données publiques et un ensemble de données privé : les données ATON Highway, les données de séquence de trafic de Karlsruhe, les données PETS 2007 et les données Walking-in-a-room.
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Kentaro YOKOI, "Probabilistic BPRRC: Robust Change Detection against Illumination Changes and Background Movements" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 7, pp. 1700-1707, July 2010, doi: 10.1587/transinf.E93.D.1700.
Abstract: This paper presents Probabilistic Bi-polar Radial Reach Correlation (PrBPRRC), a change detection method that is robust against illumination changes and background movements. Most of the traditional change detection methods are robust against either illumination changes or background movements; BPRRC is one of the illumination-robust change detection methods. We introduce a probabilistic background texture model into BPRRC and add the robustness against background movements including foreground invasions such as moving cars, walking people, swaying trees, and falling snow. We show the superiority of PrBPRRC in the environment with illumination changes and background movements by using three public datasets and one private dataset: ATON Highway data, Karlsruhe traffic sequence data, PETS 2007 data, and Walking-in-a-room data.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.1700/_p
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@ARTICLE{e93-d_7_1700,
author={Kentaro YOKOI, },
journal={IEICE TRANSACTIONS on Information},
title={Probabilistic BPRRC: Robust Change Detection against Illumination Changes and Background Movements},
year={2010},
volume={E93-D},
number={7},
pages={1700-1707},
abstract={This paper presents Probabilistic Bi-polar Radial Reach Correlation (PrBPRRC), a change detection method that is robust against illumination changes and background movements. Most of the traditional change detection methods are robust against either illumination changes or background movements; BPRRC is one of the illumination-robust change detection methods. We introduce a probabilistic background texture model into BPRRC and add the robustness against background movements including foreground invasions such as moving cars, walking people, swaying trees, and falling snow. We show the superiority of PrBPRRC in the environment with illumination changes and background movements by using three public datasets and one private dataset: ATON Highway data, Karlsruhe traffic sequence data, PETS 2007 data, and Walking-in-a-room data.},
keywords={},
doi={10.1587/transinf.E93.D.1700},
ISSN={1745-1361},
month={July},}
Copier
TY - JOUR
TI - Probabilistic BPRRC: Robust Change Detection against Illumination Changes and Background Movements
T2 - IEICE TRANSACTIONS on Information
SP - 1700
EP - 1707
AU - Kentaro YOKOI
PY - 2010
DO - 10.1587/transinf.E93.D.1700
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2010
AB - This paper presents Probabilistic Bi-polar Radial Reach Correlation (PrBPRRC), a change detection method that is robust against illumination changes and background movements. Most of the traditional change detection methods are robust against either illumination changes or background movements; BPRRC is one of the illumination-robust change detection methods. We introduce a probabilistic background texture model into BPRRC and add the robustness against background movements including foreground invasions such as moving cars, walking people, swaying trees, and falling snow. We show the superiority of PrBPRRC in the environment with illumination changes and background movements by using three public datasets and one private dataset: ATON Highway data, Karlsruhe traffic sequence data, PETS 2007 data, and Walking-in-a-room data.
ER -