抄録
We present a robust background model for object detection and its performance evaluation using the database of the Background Models Challenge (BMC). Background models should detect foreground objects robustly against background changes, such as "illumination changes" and "dynamic changes". In this paper, we propose two types of spatiotemporal background modeling frameworks that can adapt to illumination and dynamic changes in the background. Spatial information can be used to absorb the effects of illumination changes because they affect not only a target pixel but also its neighboring pixels. Additionally, temporal information is useful in handling the dynamic changes, which are observed repeatedly. To establish the spatiotemporal background model, our frameworks model an illumination invariant feature and a similarity of intensity changes among a set of pixels according to statistical models, respectively. Experimental results obtained for the BMC database show that our models can detect foreground objects robustly against background changes.
| 元の言語 | 英語 |
|---|---|
| ページ（範囲） | 84-91 |
| ページ数 | 8 |
| ジャーナル | Computer Vision and Image Understanding |
| 巻 | 122 |
| DOI | |
| 出版物ステータス | 出版済み - 5 1 2014 |
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All Science Journal Classification (ASJC) codes
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
これを引用
Object detection based on spatiotemporal background models. / Yoshinaga, Satoshi; Shimada, Atsushi; Nagahara, Hajime; Taniguchi, Rin-Ichiro.
：: Computer Vision and Image Understanding, 巻 122, 01.05.2014, p. 84-91.研究成果: ジャーナルへの寄稿 › 記事
}
TY - JOUR
T1 - Object detection based on spatiotemporal background models
AU - Yoshinaga, Satoshi
AU - Shimada, Atsushi
AU - Nagahara, Hajime
AU - Taniguchi, Rin-Ichiro
PY - 2014/5/1
Y1 - 2014/5/1
N2 - We present a robust background model for object detection and its performance evaluation using the database of the Background Models Challenge (BMC). Background models should detect foreground objects robustly against background changes, such as "illumination changes" and "dynamic changes". In this paper, we propose two types of spatiotemporal background modeling frameworks that can adapt to illumination and dynamic changes in the background. Spatial information can be used to absorb the effects of illumination changes because they affect not only a target pixel but also its neighboring pixels. Additionally, temporal information is useful in handling the dynamic changes, which are observed repeatedly. To establish the spatiotemporal background model, our frameworks model an illumination invariant feature and a similarity of intensity changes among a set of pixels according to statistical models, respectively. Experimental results obtained for the BMC database show that our models can detect foreground objects robustly against background changes.
AB - We present a robust background model for object detection and its performance evaluation using the database of the Background Models Challenge (BMC). Background models should detect foreground objects robustly against background changes, such as "illumination changes" and "dynamic changes". In this paper, we propose two types of spatiotemporal background modeling frameworks that can adapt to illumination and dynamic changes in the background. Spatial information can be used to absorb the effects of illumination changes because they affect not only a target pixel but also its neighboring pixels. Additionally, temporal information is useful in handling the dynamic changes, which are observed repeatedly. To establish the spatiotemporal background model, our frameworks model an illumination invariant feature and a similarity of intensity changes among a set of pixels according to statistical models, respectively. Experimental results obtained for the BMC database show that our models can detect foreground objects robustly against background changes.
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UR - http://www.scopus.com/inward/citedby.url?scp=84898060004&partnerID=8YFLogxK
U2 - 10.1016/j.cviu.2013.10.015
DO - 10.1016/j.cviu.2013.10.015
M3 - Article
VL - 122
SP - 84
EP - 91
JO - Computer Vision and Image Understanding
JF - Computer Vision and Image Understanding
SN - 1077-3142
ER -