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K-means Clustering Based Pixel-wise Object Tracking
https://ipsj.ixsq.nii.ac.jp/records/52019
https://ipsj.ixsq.nii.ac.jp/records/52019a214bef2-8513-49cd-9178-364b8bc82956
名前 / ファイル | ライセンス | アクション |
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Copyright (c) 2007 by the Information Processing Society of Japan
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オープンアクセス |
Item type | SIG Technical Reports(1) | |||||||
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公開日 | 2007-05-14 | |||||||
タイトル | ||||||||
タイトル | K-means Clustering Based Pixel-wise Object Tracking | |||||||
タイトル | ||||||||
言語 | en | |||||||
タイトル | K-means Clustering Based Pixel-wise Object Tracking | |||||||
言語 | ||||||||
言語 | eng | |||||||
資源タイプ | ||||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_18gh | |||||||
資源タイプ | technical report | |||||||
著者所属 | ||||||||
Faculty of System Engineering Wakayama University/Presently with the ISIR of Osaka University | ||||||||
著者所属 | ||||||||
Faculty of System Engineering Wakayama University | ||||||||
著者所属 | ||||||||
Faculty of System Engineering Wakayama University | ||||||||
著者所属 | ||||||||
Faculty of System Engineering Wakayama University | ||||||||
著者所属(英) | ||||||||
en | ||||||||
Faculty of System Engineering, Wakayama University/Presently with the ISIR of Osaka University | ||||||||
著者所属(英) | ||||||||
en | ||||||||
Faculty of System Engineering, Wakayama University | ||||||||
著者所属(英) | ||||||||
en | ||||||||
Faculty of System Engineering, Wakayama University | ||||||||
著者所属(英) | ||||||||
en | ||||||||
Faculty of System Engineering, Wakayama University | ||||||||
著者名 |
CHUNSHENG, HUA
× CHUNSHENG, HUA
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著者名(英) |
CHUNSHENG, HUA
× CHUNSHENG, HUA
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論文抄録 | ||||||||
内容記述タイプ | Other | |||||||
内容記述 | This paper brings out a robust pixel-wise object tracking algorithm which is based on the K-means clustering. In this paper the target object is assumed to be non-rigid and may contain apertures. In order to achieve the robust object tracking against such objects several ideas are applied in this work: 1) Pixel-wise clustering algorithm is applied for tracking the non-rigid object and removing the mixed background pixels from the search area; 2) Embedding the negative samples into K-means clustering so as to achieve the adaptive pixel classification without the fixed threshold; 3) Representing the image feature with a color-position feature vector so that this algorithm can follow the changes of target colors and position simultaneously; 4) A variable ellipse model is used to restrict the search area and represent the surrounding background samples; 5) Tracking failure detection and recovery processes are brought out according to both the target and background samples; 6) A radial sampling method is brought out not only for speeding up the clustering process but also improving the robustness of this algorithm. We have set up a video-rate object tracking system with the proposed algorithm. Through extensive experiments the effectiveness and advantages of this K-means clustering based tracking algorithm are confirmed. | |||||||
論文抄録(英) | ||||||||
内容記述タイプ | Other | |||||||
内容記述 | This paper brings out a robust pixel-wise object tracking algorithm which is based on the K-means clustering. In this paper, the target object is assumed to be non-rigid and may contain apertures. In order to achieve the robust object tracking against such objects, several ideas are applied in this work: 1) Pixel-wise clustering algorithm is applied for tracking the non-rigid object and removing the mixed background pixels from the search area; 2) Embedding the negative samples into K-means clustering so as to achieve the adaptive pixel classification without the fixed threshold; 3) Representing the image feature with a color-position feature vector so that this algorithm can follow the changes of target colors and position simultaneously; 4) A variable ellipse model is used to restrict the search area and represent the surrounding background samples; 5) Tracking failure detection and recovery processes are brought out according to both the target and background samples; 6) A radial sampling method is brought out not only for speeding up the clustering process but also improving the robustness of this algorithm. We have set up a video-rate object tracking system with the proposed algorithm. Through extensive experiments, the effectiveness and advantages of this K-means clustering based tracking algorithm are confirmed. | |||||||
書誌レコードID | ||||||||
収録物識別子タイプ | NCID | |||||||
収録物識別子 | AA11131797 | |||||||
書誌情報 |
情報処理学会研究報告コンピュータビジョンとイメージメディア(CVIM) 巻 2007, 号 42(2007-CVIM-159), p. 17-32, 発行日 2007-05-14 |
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Notice | ||||||||
SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. | ||||||||
出版者 | ||||||||
言語 | ja | |||||||
出版者 | 情報処理学会 |