Self-Trained Video Anomaly Detection Based on Teacher-Student Model

Xusheng Wang, Mingtao Pei, Zhengang Nie

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Anomaly detection in videos is a challenging problem in computer vision. Most existing methods need supervised information to train their models, which limits their applications in real world scenario. Therefore, self-Trained methods which do not need manually labels receive increasing attentions recently. In this paper, we propose a novel self-Trained video anomaly detection method based on teacher-student model. The teacher-student architecture can significantly improve the performance of self-Trained video anomaly detection by utilizing the unlabeled samples. We test our method on two surveillance datasets. Experiment results show that our method achieves better performance than state-of-The-Art unsupervised methods on both datasets and achieves comparable performance as semi-supervised methods, which experimentally proves the effectiveness of our method.

Original languageEnglish
Title of host publication2021 IEEE 31st International Workshop on Machine Learning for Signal Processing, MLSP 2021
PublisherIEEE Computer Society
ISBN (Electronic)9781728163383
DOIs
Publication statusPublished - 2021
Event31st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2021 - Gold Coast, Australia
Duration: 25 Oct 202128 Oct 2021

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume2021-October
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Conference

Conference31st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2021
Country/TerritoryAustralia
CityGold Coast
Period25/10/2128/10/21

Keywords

  • Anomaly Detection
  • Deep Learning
  • Self-Trained model
  • Teacher-Student model

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