Temporal information cooperated Gaussian Mixture Models for real-time surveillance with ghost detection

Tianci Huang, Chengjiao Guo, Jingbang Qiu, Takeshi Ikenaga

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

This paper describes a new real-time approach for detecting motions in the video streams taken from stationary cameras. This method combines a temporal recording scheme with the adaptive background model subtraction scheme. To save the computation brought from conventional Gaussian Mixture Models (GMM) and achieve real-time processing, an adaptively adjusted mechanism is proposed. On the other hand, illumination changes, shadow influence, and ghost in scene, these three important problems which result in low segmentation quality are settled down by utilizing proposed features and temporal information from video streams. The experimental results validate the improvement of detection accuracy. Meanwhile, the execution time for each component per frame is calculated and compared with that of conventional Gaussian Mixture Models.

本文言語English
ホスト出版物のタイトルIIH-MSP 2009 - 2009 5th International Conference on Intelligent Information Hiding and Multimedia Signal Processing
ページ1338-1341
ページ数4
DOI
出版ステータスPublished - 2009 12 1
イベントIIH-MSP 2009 - 2009 5th International Conference on Intelligent Information Hiding and Multimedia Signal Processing - Kyoto, Japan
継続期間: 2009 9 122009 9 14

出版物シリーズ

名前IIH-MSP 2009 - 2009 5th International Conference on Intelligent Information Hiding and Multimedia Signal Processing

Conference

ConferenceIIH-MSP 2009 - 2009 5th International Conference on Intelligent Information Hiding and Multimedia Signal Processing
CountryJapan
CityKyoto
Period09/9/1209/9/14

ASJC Scopus subject areas

  • Computer Science(all)

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