Unsupervised Anomaly Detection of the First Person in Gait from an Egocentric Camera

Mana Masuda, Ryo Hachiuma, Ryo Fujii, Hideo Saito

研究成果: Conference contribution

抄録

Assistive technology is increasingly important as the senior population grows. The purpose of this study is to develop a means of preventing fatal injury by monitoring the movements of the elderly and sounding an alarm if an accident occurs. We present a method of detecting an anomaly in a first-person’s gait from an egocentric video. Followed by the conventional anomaly detection methods, we train the model in an unsupervised manner. We use optical flow images to capture ego-motion information in the first person. To verify the effectiveness of our model, we introduced and conducted experiments with a novel first-person video anomaly detection dataset and showed that our model outperformed the baseline method.

本文言語English
ホスト出版物のタイトルAdvances in Visual Computing - 15th International Symposium, ISVC 2020, Proceedings
編集者George Bebis, Zhaozheng Yin, Edward Kim, Jan Bender, Kartic Subr, Bum Chul Kwon, Jian Zhao, Denis Kalkofen, George Baciu
出版社Springer Science and Business Media Deutschland GmbH
ページ604-617
ページ数14
ISBN(印刷版)9783030645588
DOI
出版ステータスPublished - 2020
イベント15th International Symposium on Visual Computing, ISVC 2020 - San Diego, United States
継続期間: 2020 10 52020 10 7

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)
12510 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference15th International Symposium on Visual Computing, ISVC 2020
CountryUnited States
CitySan Diego
Period20/10/520/10/7

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

フィンガープリント 「Unsupervised Anomaly Detection of the First Person in Gait from an Egocentric Camera」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