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An entropy approach for abnormal activities detection in video streams

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Title: An entropy approach for abnormal activities detection in video streams
Authors: Haidar Sharif, Md.1 md-haidar.sharif@gediz.edu.tr, Djeraba, Chabane2
Source: Pattern Recognition. Jul2012, Vol. 45 Issue 7, p2543-2561. 19p.
Subjects: Entropy (Information theory), Streaming video & television, Image converters, Video surveillance, Public safety, Simulation methods & models
Abstract: Abstract: Detection of aberration in video surveillance is an important task for public safety. This paper puts forward a simple but effective framework to detect aberrations in video streams using Entropy, which is estimated on the statistical treatments of the spatiotemporal information of a set of interest points within a region of interest by measuring their degree of randomness of both directions and displacements. Entropy is a measure of the disorder/randomness in video frame. It has been showed that degree of randomness of the directions (circular variance) changes markedly in abnormal state of affairs and does change only direction variation but does not change with displacement variation of the interest point. Degree of randomness of the displacements has been put in for to counterbalance this deficiency. Simple simulations have been exercised to see the characteristics of these crude elements of entropy. Normalized entropy measure provides the knowledge of the state of anomalousness. Experiments have been conducted on various real world video datasets. Both simulation and experimental results report that entropy measures of the frames over time is an outstanding way to characterize anomalies in videos. [Copyright &y& Elsevier]
Copyright of Pattern Recognition is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: An entropy approach for abnormal activities detection in video streams
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  Data: <searchLink fieldCode="JN" term="%22Pattern+Recognition%22">Pattern Recognition</searchLink>. Jul2012, Vol. 45 Issue 7, p2543-2561. 19p.
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  Data: Abstract: Detection of aberration in video surveillance is an important task for public safety. This paper puts forward a simple but effective framework to detect aberrations in video streams using Entropy, which is estimated on the statistical treatments of the spatiotemporal information of a set of interest points within a region of interest by measuring their degree of randomness of both directions and displacements. Entropy is a measure of the disorder/randomness in video frame. It has been showed that degree of randomness of the directions (circular variance) changes markedly in abnormal state of affairs and does change only direction variation but does not change with displacement variation of the interest point. Degree of randomness of the displacements has been put in for to counterbalance this deficiency. Simple simulations have been exercised to see the characteristics of these crude elements of entropy. Normalized entropy measure provides the knowledge of the state of anomalousness. Experiments have been conducted on various real world video datasets. Both simulation and experimental results report that entropy measures of the frames over time is an outstanding way to characterize anomalies in videos. [Copyright &y& Elsevier]
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  Data: <i>Copyright of Pattern Recognition is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1016/j.patcog.2011.11.023
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      – Code: eng
        Text: English
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        PageCount: 19
        StartPage: 2543
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      – SubjectFull: Entropy (Information theory)
        Type: general
      – SubjectFull: Streaming video & television
        Type: general
      – SubjectFull: Image converters
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      – SubjectFull: Video surveillance
        Type: general
      – SubjectFull: Public safety
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      – SubjectFull: Simulation methods & models
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      – TitleFull: An entropy approach for abnormal activities detection in video streams
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              Text: Jul2012
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              Y: 2012
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