Anomaly detection in crowd scenes via online adaptive one-class support vector machines

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

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

10 Citations (Scopus)

Abstract

We propose a novel, online adaptive one-class support vector machines algorithm for anomaly detection in crowd scenes. Integrating incremental and decremental one-class support vector machines with a sliding buffer offers an efficient and effective scheme, which not only updates the model in an online fashion with low computational cost, but also discards obsolete patterns. Our method provides a unified framework to detect both global and local anomalies. Extensive experiments have been carried out on two benchmark datasets and the comparison to the state-of-the-art methods validates the advantages of our approach.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Image Processing (ICIP)
Subtitle of host publicationProceedings
PublisherIEEE
Pages2434-2438
Number of pages5
ISBN (Electronic)978-1-4799-8339-1
ISBN (Print)978-1-4799-8338-4
DOIs
Publication statusPublished - 10 Dec 2015
EventIEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada
Duration: 27 Sept 201530 Sept 2015

Publication series

NameProceedings - International Conference on Image Processing
PublisherIEEE
Volume2015-December
ISSN (Print)1522-4880

Conference

ConferenceIEEE International Conference on Image Processing, ICIP 2015
Country/TerritoryCanada
CityQuebec City
Period27/09/1530/09/15

Keywords

  • anomaly detection
  • crowd scenes
  • online learning
  • support vector machines

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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