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

Mana Masuda, Ryo Hachiuma, Ryo Fujii, Hideo Saito

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationAdvances in Visual Computing - 15th International Symposium, ISVC 2020, Proceedings
EditorsGeorge Bebis, Zhaozheng Yin, Edward Kim, Jan Bender, Kartic Subr, Bum Chul Kwon, Jian Zhao, Denis Kalkofen, George Baciu
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages14
ISBN (Print)9783030645588
Publication statusPublished - 2020
Event15th International Symposium on Visual Computing, ISVC 2020 - San Diego, United States
Duration: 2020 Oct 52020 Oct 7

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12510 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th International Symposium on Visual Computing, ISVC 2020
Country/TerritoryUnited States
CitySan Diego


  • Adversarial training
  • Assistive technology
  • Egocentric video
  • Optical flow
  • Unsupervised learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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