Online weighted clustering for real-time abnormal event detection in video surveillance

Hanhe Lin, Jeremiah D. Deng, Brendon J. Woodford, Ahmad Shahi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Citations (Scopus)

Abstract

Detecting abnormal events in video surveillance is a challenging problem due to the large scale, stream fashion video data as well as the real-time constraint. In this paper, we present an online, adaptive, and real-time framework to address this problem. The spatial locations in a frame is partitioned into grids, in each grid the proposed Adaptive Multi-scale Histogram Optical Flow (AMHOF) features are extracted and modelled by an Online Weighted Clustering (OWC) algorithm. The AMHOFs which cannot be fit to a cluster with large weight are regarded as abnormal events. The OWC algorithm is simple to implement and computational efficient. In addition, we improve the detection performance by a Multiple Target Tracking (MTT) algorithm. Experimental results demonstrate our approach outperforms the state-of-the-art approaches in pixel-level rate of detection at a processing speed of 30 FPS.

Original languageEnglish
Title of host publicationMM 2016
Subtitle of host publicationProceedings of the 2016 ACM Multimedia Conference
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery
Pages536-540
Number of pages5
ISBN (Electronic)978-1-4503-3603-1
DOIs
Publication statusPublished - 1 Oct 2016
Event24th ACM Multimedia Conference, MM 2016 - Amsterdam, United Kingdom
Duration: 15 Oct 201619 Oct 2016

Publication series

NameMM 2016 - Proceedings of the 2016 ACM Multimedia Conference

Conference

Conference24th ACM Multimedia Conference, MM 2016
Country/TerritoryUnited Kingdom
CityAmsterdam
Period15/10/1619/10/16

Keywords

  • Abnormal event detection
  • Multiple target tracking
  • Online adaptive learning
  • Real-time
  • Video surveillance

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