VisLoiter+: An entropy model-based loiterer retrieval system with user-friendly interfaces

Maguell L.T.L. Sandifort, Jianquan Liu, Shoji Nishimura, Wolfgang Hürst

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

Abstract

It is very difficult to fully automate the detection of loitering behavior in video surveillance, therefore humans are often required for monitoring. Alternatively, we could provide a list of potential loiterer candidates for a final yes/no judgment of a human operator. Our system, VisLoiter+, realizes this idea with a unique, user-friendly interface and by employing an entropy model for improved loitering analysis. Rather than using only frequency of appearance, we expand the loiter analysis with new methods measuring the amount of person movements across multiple camera views. The interface gives an overview of loiterer candidates to show their behavior at a glance, complemented by a lightweight video playback for further details about why a candidate was selected. We demonstrate that our system outperforms state-of-the-art solutions using real-life data sets.

Original languageEnglish
Title of host publicationICMR 2018 - Proceedings of the 2018 ACM International Conference on Multimedia Retrieval
PublisherAssociation for Computing Machinery
Pages505-508
Number of pages4
ISBN (Print)9781450350464
DOIs
Publication statusPublished - 5 Jun 2018
Event8th ACM International Conference on Multimedia Retrieval, ICMR 2018 - Yokohama, Japan
Duration: 11 Jun 201814 Jun 2018

Conference

Conference8th ACM International Conference on Multimedia Retrieval, ICMR 2018
Country/TerritoryJapan
CityYokohama
Period11/06/1814/06/18

Keywords

  • Entropy model
  • Heatmap
  • Loiterer retrieval
  • Loitering discovery
  • Ranking system
  • Video surveillance

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