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 PaperResearch

3 Citations (Scopus)

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)
EditorsGang Hua, Ming-Yu Liu, Vishal Patel, Walter Scheirer, Ryan Farrell
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages126-133
Number of pages8
ISBN (Electronic)9781728171623
ISBN (Print)9781728171630
DOIs
Publication statusPublished - 2020
EventIEEE Winter Conference on Applications of Computer Vision Workshops 2020 - Snowmass Village, United States of America
Duration: 1 Mar 20205 Mar 2020

Conference

ConferenceIEEE Winter Conference on Applications of Computer Vision Workshops 2020
Abbreviated titleWACVW 2020
CountryUnited States of America
CitySnowmass Village
Period1/03/205/03/20

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