Real-time Multiple Abnormality Detection in Video Data

Simon Hartmann Have, Huamin Ren, Thomas B. Moeslund

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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Abstract

Automatic abnormality detection in video sequences has recently gained an increasing attention within the research community. Although progress has been seen, there are still some limitations in current research. While most systems are designed at detecting specific abnormality, others which are capable of detecting more than two types of abnormalities rely on heavy computation. Therefore, we provide a framework for detecting abnormalities in video surveillance by using multiple features and cascade classifiers, yet achieve above real-time processing speed. Experimental results on two datasets show that the proposed framework can reliably detect abnormalities in the video sequence, outperforming the current state-of-the-art methods.
Original languageEnglish
Title of host publicationINSTICC : The International Conference on Computer Vision Theory and Applications
Number of pages6
PublisherInstitute for Systems and Technologies of Information, Control and Communication
Publication date2013
Article number140
Publication statusPublished - 2013
EventVISAPP 2013 - Barcelona, Spain
Duration: 21 Feb 201324 Feb 2013
Conference number: 8
http://www.visapp.visigrapp.org/?y=2013

Conference

ConferenceVISAPP 2013
Number8
CountrySpain
CityBarcelona
Period21/02/201324/02/2013
Internet address

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Classifiers
Processing

Keywords

  • Abnormality detection; Cascade classifier; Video surveillance; Optical flow.

Cite this

Have, S. H., Ren, H., & Moeslund, T. B. (2013). Real-time Multiple Abnormality Detection in Video Data. In INSTICC: The International Conference on Computer Vision Theory and Applications [140] Institute for Systems and Technologies of Information, Control and Communication.
Have, Simon Hartmann ; Ren, Huamin ; Moeslund, Thomas B. / Real-time Multiple Abnormality Detection in Video Data. INSTICC: The International Conference on Computer Vision Theory and Applications. Institute for Systems and Technologies of Information, Control and Communication, 2013.
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title = "Real-time Multiple Abnormality Detection in Video Data",
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Have, SH, Ren, H & Moeslund, TB 2013, Real-time Multiple Abnormality Detection in Video Data. in INSTICC: The International Conference on Computer Vision Theory and Applications., 140, Institute for Systems and Technologies of Information, Control and Communication, VISAPP 2013, Barcelona, Spain, 21/02/2013.

Real-time Multiple Abnormality Detection in Video Data. / Have, Simon Hartmann; Ren, Huamin; Moeslund, Thomas B.

INSTICC: The International Conference on Computer Vision Theory and Applications. Institute for Systems and Technologies of Information, Control and Communication, 2013. 140.

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

TY - GEN

T1 - Real-time Multiple Abnormality Detection in Video Data

AU - Have, Simon Hartmann

AU - Ren, Huamin

AU - Moeslund, Thomas B.

PY - 2013

Y1 - 2013

N2 - Automatic abnormality detection in video sequences has recently gained an increasing attention within the research community. Although progress has been seen, there are still some limitations in current research. While most systems are designed at detecting specific abnormality, others which are capable of detecting more than two types of abnormalities rely on heavy computation. Therefore, we provide a framework for detecting abnormalities in video surveillance by using multiple features and cascade classifiers, yet achieve above real-time processing speed. Experimental results on two datasets show that the proposed framework can reliably detect abnormalities in the video sequence, outperforming the current state-of-the-art methods.

AB - Automatic abnormality detection in video sequences has recently gained an increasing attention within the research community. Although progress has been seen, there are still some limitations in current research. While most systems are designed at detecting specific abnormality, others which are capable of detecting more than two types of abnormalities rely on heavy computation. Therefore, we provide a framework for detecting abnormalities in video surveillance by using multiple features and cascade classifiers, yet achieve above real-time processing speed. Experimental results on two datasets show that the proposed framework can reliably detect abnormalities in the video sequence, outperforming the current state-of-the-art methods.

KW - Abnormality detection; Cascade classifier; Video surveillance; Optical flow.

M3 - Article in proceeding

BT - INSTICC

PB - Institute for Systems and Technologies of Information, Control and Communication

ER -

Have SH, Ren H, Moeslund TB. Real-time Multiple Abnormality Detection in Video Data. In INSTICC: The International Conference on Computer Vision Theory and Applications. Institute for Systems and Technologies of Information, Control and Communication. 2013. 140