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  1. A500 情報学部/情報学研究科・情報文化学部・情報科学研究科
  2. A500a 雑誌掲載論文
  3. 学術雑誌

An HMM/MRF-based stochastic framework for robust vehicle tracking

http://hdl.handle.net/2237/6743
2e8bcb9d-9164-4083-a16d-38ae4aaf6b9b
名前 / ファイル ライセンス アクション
01331385.pdf 01331385.pdf (1.0 MB)
Item type 学術雑誌論文 / Journal Article(1)
公開日 2006-07-26
タイトル
タイトル An HMM/MRF-based stochastic framework for robust vehicle tracking
著者 Kato, Jien

× Kato, Jien

WEKO 12905

Kato, Jien

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Watanabe, Toyohide

× Watanabe, Toyohide

WEKO 12906

Watanabe, Toyohide

Search repository
Joga, Sébastien

× Joga, Sébastien

WEKO 12907

Joga, Sébastien

Search repository
Ying, Liu

× Ying, Liu

WEKO 12908

Ying, Liu

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Hase, Hiroyuki

× Hase, Hiroyuki

WEKO 12909

Hase, Hiroyuki

Search repository
加藤, ジェーン

× 加藤, ジェーン

WEKO 12910

加藤, ジェーン

Search repository
渡邉, 豊英

× 渡邉, 豊英

WEKO 12911

渡邉, 豊英

Search repository
権利
権利情報 Copyright © 2004 IEEE. Reprinted from (relevant publication info). This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Nagoya University’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org.
キーワード
主題Scheme Other
主題 Hidden Markov model (HMM)
キーワード
主題Scheme Other
主題 image classification
キーワード
主題Scheme Other
主題 image segmentation
キーワード
主題Scheme Other
主題 Markov random field (MRF)
キーワード
主題Scheme Other
主題 traffic surveillance
キーワード
主題Scheme Other
主題 vehicle tracking
抄録
内容記述 Shadows of moving objects often obstruct robust visual tracking. In this paper, we present a car tracker based on a hidden Markov model/Markov random field (HMM/MRF)-based segmentation method that is capable of classifying each small region
of an image into three different categories: vehicles, shadows of vehicles, and background from a traffic-monitoring movie. The temporal continuity of the different categories for one small region location is modeled as a single HMM along the time axis, independently of the neighboring regions. In order to incorporate
spatial-dependent information among neighboring regions into the tracking process, at the state-estimation stage, the output from the HMMs is regarded as an MRF and the maximum a posteriori criterion is employed in conjunction with the MRF for optimization. At each time step, the state estimation for the image is equivalent to the optimal configuration of the MRF generated through a stochastic relaxation process. Experimental results show that, using this method, foreground (vehicles) and nonforeground regions including the shadows of moving vehicles can be discriminated with high accuracy.
内容記述タイプ Abstract
出版者
出版者 IEEE
言語
言語 eng
資源タイプ
資源タイプresource http://purl.org/coar/resource_type/c_6501
タイプ journal article
DOI
関連識別子
識別子タイプ DOI
関連識別子 http://dx.doi.org/10.1109/TITS.2004.833791
ISSN
収録物識別子タイプ ISSN
収録物識別子 1524-9050
書誌情報 IEEE Transactions on Intelligent Transportation Systems

巻 5, 号 3, p. 142-154, 発行日 2004-09
フォーマット
application/pdf
著者版フラグ
値 publisher
URI
識別子 http://hdl.handle.net/2237/6743
識別子タイプ HDL
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