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K-means tracker: A multiple colors object tracking algorithm
https://ipsj.ixsq.nii.ac.jp/records/52297
https://ipsj.ixsq.nii.ac.jp/records/522972f6845c9-4ffc-44b5-a3c3-a5178652312c
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Copyright (c) 2005 by the Information Processing Society of Japan
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Item type | SIG Technical Reports(1) | |||||||
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公開日 | 2005-11-17 | |||||||
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タイトル | K-means tracker: A multiple colors object tracking algorithm | |||||||
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言語 | en | |||||||
タイトル | K-means tracker: A multiple colors object tracking algorithm | |||||||
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言語 | eng | |||||||
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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 | ||||||||
Wakayama University | ||||||||
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Wakayama University | ||||||||
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Wakayama University | ||||||||
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Wakayama University | ||||||||
著者所属 | ||||||||
Wakayama University | ||||||||
著者所属(英) | ||||||||
en | ||||||||
Wakayama University | ||||||||
著者所属(英) | ||||||||
en | ||||||||
Wakayama University | ||||||||
著者所属(英) | ||||||||
en | ||||||||
Wakayama University | ||||||||
著者所属(英) | ||||||||
en | ||||||||
Wakayama University | ||||||||
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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 presents a K-means tracker which is a novel visual tracking algorithm. This algorithm is robust against the interfused background because it discriminates "target" Pixels Inn "background" pixels in the tracking region by applying K-means clustering to both the positive and negative information of the target. To ensure the robustness of this algorithm we apply the following ideas: 1) To represent the color similarity and spatial approximation of the target simultaneously we use a 5D feature vector consisting of the position (x y) and color (y u v) information for object tracking. The object tracking is performed and updated not only in the image space but also in the color space at the same time. Therefore this adaptive nature guarantees our method to be robust against the target color cIn1ge. 2) By using a variable ellipse model to restrict the target search area and represent the non-target pixels surrounding the target the algorithm can cope with the changes of the scale and shape of the target object flexibly. 3) Even (he tracking sometimes fails this algorithm can automatically discover and recover from the tracking failure based on the positive and negative Information. To capture motion-blur-free images of a moving object at video rate we control a set of active cameras which are mounted on the pan-tilt units according to the results of the K-means tracker. | |||||||
論文抄録(英) | ||||||||
内容記述タイプ | Other | |||||||
内容記述 | This paper presents a K-means tracker, which is a novel visual tracking algorithm. This algorithm is robust against the interfused background, because it discriminates "target" Pixels Inn "background" pixels in the tracking region by applying K-means clustering to both the positive and negative information of the target. To ensure the robustness of this algorithm, we apply the following ideas: 1) To represent the color similarity and spatial approximation of the target simultaneously, we use a 5D feature vector consisting of the position (x, y) and color (y, u, v) information for object tracking. The object tracking is performed and updated not only in the image space but also in the color space at the same time. Therefore, this adaptive nature guarantees our method to be robust against the target color cIn1ge. 2) By using a variable ellipse model to restrict the target search area and represent the non-target pixels surrounding the target, the algorithm can cope with the changes of the scale and shape of the target object flexibly. 3) Even (he tracking sometimes fails, this algorithm can automatically discover and recover from the tracking failure based on the positive and negative Information. To capture motion-blur-free images of a moving object at video rate, we control a set of active cameras which are mounted on the pan-tilt units according to the results of the K-means tracker. | |||||||
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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 2005, 号 112(2005-CVIM-151), p. 1-8, 発行日 2005-11-17 |
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Notice | ||||||||
SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. | ||||||||
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言語 | ja | |||||||
出版者 | 情報処理学会 |