Towards robust object detection: Integrated background modeling based on spatio-temporal features

Tatsuya Tanaka, Atsushi Shimada, Rin Ichiro Taniguchi, Takayoshi Yamashita, Daisaku Arita

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

9 被引用数 (Scopus)

抄録

We propose a sophisticated method for background modeling based on spatio-temporal features. It consists of three complementary approaches: pixel-level background modeling, region-level one and frame-level one. The pixel-level background model uses the probability density function to approximate background model. The PDF is estimated non-parametrically by using Parzen density estimation. The region-level model is based on the evaluation of the local texture around each pixel while reducing the effects of variations in lighting. The frame-level model detects sudden, global changes of the the image brightness and estimates a present background image from input image referring to a background model image. Then, objects are extracted by background subtraction. Fusing their approaches realizes robust object detection under varying illumination, which is shown in several experiments.

本文言語英語
ホスト出版物のタイトルComputer Vision, ACCV 2009 - 9th Asian Conference on Computer Vision, Revised Selected Papers
ページ201-202
ページ数2
PART 1
DOI
出版ステータス出版済み - 2010
イベント9th Asian Conference on Computer Vision, ACCV 2009 - Xi'an, 中国
継続期間: 9月 23 20099月 27 2009

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
番号PART 1
5994 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

その他

その他9th Asian Conference on Computer Vision, ACCV 2009
国/地域中国
CityXi'an
Period9/23/099/27/09

!!!All Science Journal Classification (ASJC) codes

  • 理論的コンピュータサイエンス
  • コンピュータサイエンス一般

フィンガープリント

「Towards robust object detection: Integrated background modeling based on spatio-temporal features」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル