Non-parametric background and shadow modeling for object detection

Tatsuya Tanaka, Atsushi Shimada, Daisaku Arita, Rin-Ichiro Taniguchi

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

22 被引用数 (Scopus)

抄録

We propose a fast algorithm to estimate background models using Parzen density estimation in non-stationary scenes. Each pixel has a probability density which approximates pixel values observed in a video sequence. It is important to estimate a probability density function fast and accurately. In our approach, the probability density function is partially updated within the range of the window function based on the observed pixel value. The model adapts quickly to changes in the scene and foreground objects can be robustly detected. In addition, applying our approach to cast-shadow modeling, we can detect moving cast shadows. Several experiments show the effectiveness of our approach.

本文言語英語
ホスト出版物のタイトルComputer Vision - ACCV 2007 - 8th Asian Conference on Computer Vision, Proceedings
ページ159-168
ページ数10
PART 1
出版ステータス出版済み - 12 1 2007
イベント8th Asian Conference on Computer Vision, ACCV 2007 - Tokyo, 日本
継続期間: 11 18 200711 22 2007

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
4843 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

その他

その他8th Asian Conference on Computer Vision, ACCV 2007
Country日本
CityTokyo
Period11/18/0711/22/07

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

フィンガープリント 「Non-parametric background and shadow modeling for object detection」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