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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 조명 변화와 배경 움직임에 강한 변화 감지 방법인 확률적 양극 방사형 도달 상관관계(PrBPRRC)를 제시합니다. 대부분의 기존 변화 감지 방법은 조명 변화나 배경 움직임에 강력합니다. BPRRC는 조명에 강한 변화 감지 방법 중 하나입니다. BPRRC에 확률적 배경 텍스처 모델을 도입하고 움직이는 자동차, 걷는 사람, 흔들리는 나무, 떨어지는 눈과 같은 전경 침입을 포함한 배경 움직임에 대한 견고성을 추가합니다. ATON Highway 데이터, Karlsruhe 교통 순서 데이터, PETS 2007 데이터, Walking-in-a-room 데이터 등 XNUMX개의 공개 데이터 세트와 XNUMX개의 개인 데이터 세트를 사용하여 조명 변화 및 배경 움직임이 있는 환경에서 PrBPRRC의 우수성을 보여줍니다.
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부
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
부
@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},}
부
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 -