Abnormal behavior detection using privacy protected videos

Yumi Iwashita, Shuhei Takaki, Kenichi Morooka, Tokuo Tsuji, Ryo Kurazume

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

3 被引用数 (Scopus)

抄録

Intelligent visual surveillance, which relies heavily on human motion detection / recognition and people recognition, has received a lot of attention for its use in effective monitoring of public places. However, there is a concern of loss of privacy due to distinguishable facial and body information. To deal with this issue, researchers proposed to protect privacy example by filtering of face or body areas, and developed methods of people identification from videos in which people's faces has been obfuscated, masked by digital filters. Along the same line of research dealing with videos in which the people faces were masked by filters, this paper introduces a method to detect abnormal behavior. In the proposed method, we first mask face areas in videos by Multiple Instance Learning tracking, and extract silhouette area from each image. We then extract features using affine moment invariants, and perform classification. We build a database including normal and abnormal behaviors, and we show the effectiveness of the proposed method on cases from the database.

本文言語英語
ホスト出版物のタイトルProceedings - 2013 4th International Conference on Emerging Security Technologies, EST 2013
ページ55-57
ページ数3
DOI
出版ステータス出版済み - 12 1 2013
イベント2013 4th International Conference on Emerging Security Technologies, EST 2013 - Cambridge, 英国
継続期間: 9 9 20139 11 2013

出版物シリーズ

名前Proceedings - 2013 4th International Conference on Emerging Security Technologies, EST 2013

その他

その他2013 4th International Conference on Emerging Security Technologies, EST 2013
Country英国
CityCambridge
Period9/9/139/11/13

All Science Journal Classification (ASJC) codes

  • Software

フィンガープリント 「Abnormal behavior detection using privacy protected videos」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