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Argus: Efficient Activity Detection System for Extended Video Analysis

  • Wenhe Liu
  • , Guoliang Kang
  • , Po Yao Huang
  • , Xiaojun Chang
  • , Lijun Yu
  • , Yijun Qian
  • , Junwei Liang
  • , Liangke Gui
  • , Jing Wen
  • , Peng Chen
  • , Alexander G. Hauptmann

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

Abstract

We propose an Efficient Activity Detection System, Argus, for Extended Video Analysis in the surveillance scenario. For the spatial-temporal event detection in the surveillance video, we first generate video proposals by applying object detection and tracking algorithm which shared the detection features. After that, we extract several different features and apply sequential activity classification with them. Finally, we eliminate inaccurate events and fuse all the predictions from different features. The proposed system wins Trecvid Activities in Extended Video (ActEV1) challenge 2019. It achieves the first place with 60.5 mean weighted Pmiss, outperforming the second place system by 14.5 and the baseline R-C3D by 29.0. In TRECVID 2019 Challenge2, the proposed system wins the first place with pAUDC@0.2tfa 0.48407.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE Winter Conference on Applications of Computer Vision Workshops, WACVW 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages126-133
Number of pages8
ISBN (Electronic)9781728171623
DOIs
StatePublished - Mar 2020
Externally publishedYes
Event2020 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2020 - Snowmass Village, United States
Duration: 1 Mar 20205 Mar 2020

Publication series

NameProceedings - 2020 IEEE Winter Conference on Applications of Computer Vision Workshops, WACVW 2020

Conference

Conference2020 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2020
Country/TerritoryUnited States
CitySnowmass Village
Period1/03/205/03/20

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