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Real-time detection of unusual regions in image streams

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Luc Van GoolAuthors Info & Claims

MM '10: Proceedings of the 18th ACM international conference on Multimedia

Pages 1307 - 1310

https://doi.org/10.1145/1873951.1874208

Published: 25 October 2010 Publication History

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Abstract

Automatic and real-time identification of unusual incidents is important for event detection and alarm systems. In today's camera surveillance solutions video streams are displayed on-screen for human operators, e.g. in large multi-screen control centers. This in turn requires the attention of operators for unusual events and urgent response.

This paper presents a method for the automatic identification of unusual visual content in video streams real-time. In contrast to explicitly modeling specific unusual events, the proposed approach incrementally learns the usual appearances from the visual source and simultaneously identifies potential unusual image regions in the scene. Experiments demonstrate the general applicability on a variety of large-scale datasets including different scenes from public web cams and from traffic monitoring. To further demonstrate the real-time capabilities of the unusual scene detection we actively control a Pan-Tilt-Zoom camera to get close up views of the unusual incidents.

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Cited By

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Maître H(2023)ReferencesAesthetics in Digital Photography10.1002/9781394225972.refs(271-294)Online publication date: 14-Jul-2023
https://doi.org/10.1002/9781394225972.refs
Helala MQureshi FPu K(2020)A Stream Algebra for Performance Optimization of Large Scale Computer Vision PipelinesIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2020.3015867(1-1)Online publication date: 2020
https://doi.org/10.1109/TPAMI.2020.3015867
Ahmed TPathan AAhmed S(2017)Learning Algorithms for Anomaly Detection from ImagesBiometrics10.4018/978-1-5225-0983-7.ch013(281-308)Online publication date: 2017
https://doi.org/10.4018/978-1-5225-0983-7.ch013
Show More Cited By

Index Terms

Real-time detection of unusual regions in image streams
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Tracking
      2. Computer vision tasks
        Scene understanding
  2. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis

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Published In

MM '10: Proceedings of the 18th ACM international conference on Multimedia

October 2010

1836 pages

ISBN:9781605589336

DOI:10.1145/1873951

General Chairs:
Alberto del Bimbo
University of Florence, Italy
,
Shih-Fu Chang
Columbia University, USA
,
Program Chair:
Arnold Smeulders
University of Amsterdam, NL

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 October 2010

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MM '10: ACM Multimedia Conference

October 25 - 29, 2010

Firenze, Italy

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Maître H(2023)ReferencesAesthetics in Digital Photography10.1002/9781394225972.refs(271-294)Online publication date: 14-Jul-2023
https://doi.org/10.1002/9781394225972.refs
Helala MQureshi FPu K(2020)A Stream Algebra for Performance Optimization of Large Scale Computer Vision PipelinesIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2020.3015867(1-1)Online publication date: 2020
https://doi.org/10.1109/TPAMI.2020.3015867
Ahmed TPathan AAhmed S(2017)Learning Algorithms for Anomaly Detection from ImagesBiometrics10.4018/978-1-5225-0983-7.ch013(281-308)Online publication date: 2017
https://doi.org/10.4018/978-1-5225-0983-7.ch013
Anika AKarim KMuntaha RShahrear FAhmed SAhmed T(2017)Multi image retrieval for kernel-based automated intruder detection2017 IEEE Region 10 Symposium (TENSYMP)10.1109/TENCONSpring.2017.8070045(1-5)Online publication date: Jul-2017
https://doi.org/10.1109/TENCONSpring.2017.8070045
Yun KYoo YChoi J(2017)Motion interaction field for detection of abnormal interactionsMachine Vision and Applications10.1007/s00138-016-0816-028:1-2(157-171)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1007/s00138-016-0816-0
Ahmed TPathan AAhmed S(2015)Learning Algorithms for Anomaly Detection from ImagesInternational Journal of System Dynamics Applications10.4018/IJSDA.20150701034:3(43-69)Online publication date: 1-Jul-2015
https://dl.acm.org/doi/10.4018/IJSDA.2015070103
Ahmed TPathan AAhmed S(2014)Adaptive algorithms for automated intruder detection in surveillance networks2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI)10.1109/ICACCI.2014.6968617(2775-2780)Online publication date: Sep-2014
https://doi.org/10.1109/ICACCI.2014.6968617
Helala MPu KQureshi F(2014)A Stream Algebra for Computer Vision PipelinesProceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops10.1109/CVPRW.2014.122(800-807)Online publication date: 23-Jun-2014
https://dl.acm.org/doi/10.1109/CVPRW.2014.122
Yun KKim JKim SJeong HChoi J(2014)Learning with Adaptive Rate for Online Detection of Unusual AppearanceAdvances in Visual Computing10.1007/978-3-319-14249-4_67(698-707)Online publication date: 2014
https://doi.org/10.1007/978-3-319-14249-4_67
Metternich MWorring M(2013)Track based relevance feedback for tracing persons in surveillance videosComputer Vision and Image Understanding10.1016/j.cviu.2012.11.004117:3(229-237)Online publication date: 1-Mar-2013
https://dl.acm.org/doi/10.1016/j.cviu.2012.11.004
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Distributed real-time soccer tracking

Robust Real-Time Unusual Event Detection using Multiple Fixed-Location Monitors

Towards an advanced system for real-time event detection in high-volume data streams

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