Large scale hierarchical anomaly detection and temporal localization

Soumil Kanwal, Vineet Mehta, Abhinav Dhall

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

1 Citation (Scopus)

Abstract

Abnormal event detection is a non-trivial task in machine learning. The primary reason behind this is that the abnormal class occurs sparsely, and its temporal location may not be available. In this paper, we propose a multiple feature-based approach for CitySCENE challenge-based anomaly detection. For motion and context information, Res3D and Res101 architectures are used. Object-level information is extracted by object detection feature-based pooling. Fusion of three channels above gives relatively high performance on the challenge Test set for the general anomaly task. We also show how our method can be used for temporal localisation of the abnormal activity event in a video.

Original languageEnglish
Title of host publicationProceedings of the 28th ACM International Conference on Multimedia
EditorsGuo-Jun Qi, Elisa Ricci, Zhengyou Zhang, Roger Zimmermann
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages4674-4678
Number of pages5
ISBN (Electronic)9781450379885
DOIs
Publication statusPublished - 2020
EventACM International Conference on Multimedia 2020 - Virtual, Online, United States of America
Duration: 12 Oct 202016 Oct 2020
Conference number: 28th
https://dl.acm.org/doi/proceedings/10.1145/3394171 (Proceedings)

Conference

ConferenceACM International Conference on Multimedia 2020
Abbreviated titleMM 2020
Country/TerritoryUnited States of America
CityVirtual, Online
Period12/10/2016/10/20
Internet address

Keywords

  • anomaly detection
  • CitySCENE
  • convolutional neural networks

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