Evidential Reasoning for Video Anomaly Detection

Che Sun, Yunde Jia, Yuwei Wu*

*Corresponding author for this work

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

4 Citations (Scopus)

Abstract

Video anomaly detection aims to discriminate events that deviate from normal patterns in a video. Modeling the decision boundaries of anomalies is challenging, due to the uncertainty in the probability of deviating from normal patterns. In this paper, we propose a deep evidential reasoning method that explicitly learns the uncertainty to model the boundaries. Our method encodes various visual cues as evidences representing potential deviations, assigns beliefs to the predicted probability of deviating from normal patterns based on the evidences, and estimates the uncertainty from the remained beliefs to model the boundaries. To do this, we build a deep evidential reasoning network to encode evidence vectors and estimate uncertainty by learning evidence distributions and deriving beliefs from the distributions. We introduce an unsupervised strategy to train our network by minimizing an energy function of the deep Gaussian mixed model (GMM). Experimental results show that our uncertainty score is beneficial for modeling the boundaries of video anomalies on three benchmark datasets.

Original languageEnglish
Title of host publicationMM 2022 - Proceedings of the 30th ACM International Conference on Multimedia
PublisherAssociation for Computing Machinery, Inc
Pages2106-2114
Number of pages9
ISBN (Electronic)9781450392037
DOIs
Publication statusPublished - 10 Oct 2022
Event30th ACM International Conference on Multimedia, MM 2022 - Lisboa, Portugal
Duration: 10 Oct 202214 Oct 2022

Publication series

NameMM 2022 - Proceedings of the 30th ACM International Conference on Multimedia

Conference

Conference30th ACM International Conference on Multimedia, MM 2022
Country/TerritoryPortugal
CityLisboa
Period10/10/2214/10/22

Keywords

  • deep Gaussian mixed model
  • evidential reasoning
  • uncertainty estimation
  • video anomaly detection

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