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Robust Object Detection in Severe Imaging Conditions using Co-Occurrence Background Model

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/57664

Title: Robust Object Detection in Severe Imaging Conditions using Co-Occurrence Background Model
Authors: Liang, Dong Browse this author
Kaneko, Shun'ichi Browse this author →KAKEN DB
Hashimoto, Manabu Browse this author
Iwata, Kenji Browse this author
Zhao, Xinyue Browse this author
Satoh, Yutaka Browse this author
Keywords: background modeling
low-illumination
narrow dynamic range
object detection
underexposure
Issue Date: Jan-2014
Publisher: Taylor & Francis
Journal Title: International Journal of Optomechatronics
Volume: 8
Issue: 1
Start Page: 14
End Page: 29
Publisher DOI: 10.1080/15599612.2014.890686
Abstract: In this study, a spatial-dependent background model for detecting objects is used in severe imaging conditions. It is robust in the cases of sudden illumination fluctuation and burst motion background. More importantly, it is quite sensitive under the cases of underexposure, low-illumination, and narrow dynamic range, all of which are very common phenomenon using a surveillance camera. The background model maintains statistical models in the form of multiple pixel pairs with few parameters. Experiments using several challenging datasets (Heavy Fog, PETS-2001, AIST-INDOOR, and a real surveillance application) confirm the robust performance in various imaging conditions.
Rights: This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Optomechatronics on vol. 8, no. 1, 2014, available online: http://www.tandfonline.com/10.1080/15599612.2014.890686.
Type: article (author version)
URI: http://hdl.handle.net/2115/57664
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 金子 俊一

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