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An HMM/MRF-based stochastic framework for robust vehicle tracking
http://hdl.handle.net/2237/6743
http://hdl.handle.net/2237/67432e8bcb9d-9164-4083-a16d-38ae4aaf6b9b
名前 / ファイル | ライセンス | アクション |
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01331385.pdf (1.0 MB)
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Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2006-07-26 | |||||
タイトル | ||||||
タイトル | An HMM/MRF-based stochastic framework for robust vehicle tracking | |||||
言語 | en | |||||
著者 |
Kato, Jien
× Kato, Jien× Watanabe, Toyohide× Joga, Sébastien× Ying, Liu× Hase, Hiroyuki× 加藤, ジェーン× 渡邉, 豊英 |
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アクセス権 | ||||||
アクセス権 | open access | |||||
アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||
権利 | ||||||
言語 | en | |||||
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. | |||||
言語 | en | |||||
内容記述タイプ | Abstract | |||||
出版者 | ||||||
言語 | en | |||||
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 | |||||
出版タイプ | ||||||
出版タイプ | VoR | |||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
DOI | ||||||
関連タイプ | isVersionOf | |||||
識別子タイプ | DOI | |||||
関連識別子 | https://doi.org/10.1109/TITS.2004.833791 | |||||
ISSN | ||||||
収録物識別子タイプ | PISSN | |||||
収録物識別子 | 1524-9050 | |||||
書誌情報 |
en : IEEE Transactions on Intelligent Transportation Systems 巻 5, 号 3, p. 142-154, 発行日 2004-09 |
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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 |