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Analysing animal behaviour in wildlife videos using face detection and tracking.

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Title: Analysing animal behaviour in wildlife videos using face detection and tracking.
Authors: Burghardt, T.1, Ćalić, J.1 janko@cs.bris.ac.uk
Source: IEE Proceedings -- Vision, Image & Signal Processing. Jun2006, Vol. 153 Issue 3, p305-312. 8p. 4 Diagrams, 3 Charts, 6 Graphs.
Subjects: Animal behavior, Algorithms, Wildlife films, Tracking & trailing, Signal detection, Animal species
Abstract: An algorithm that categorises animal locomotive behaviour by combining detection and tracking of animal faces in wildlife videos is presented. As an example, the algorithm is applied to lion faces. The detection algorithm is based on a human face detection method, utilising Haar-like features and AdaBoost classifiers. The face tracking is implemented by applying a specific interest model that combines low-level feature tracking with the detection algorithm. By combining the two methods in a specific tracking model, reliable and temporally coherent detection/tracking of animal faces is achieved. The information generated by the tracker is used to automatically annotate the animal's locomotive behaviour. The annotation classes of locomotive processes for a given animal species are predefined by a large semantic taxonomy on wildlife domain. The experimental results are presented. [ABSTRACT FROM AUTHOR]
Copyright of IEE Proceedings -- Vision, Image & Signal Processing is the property of Institution of Engineering & Technology 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
An: 20800387
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Analysing animal behaviour in wildlife videos using face detection and tracking.
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  Data: <searchLink fieldCode="AR" term="%22Burghardt%2C+T%2E%22">Burghardt, T.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Ćalić%2C+J%2E%22">Ćalić, J.</searchLink><relatesTo>1</relatesTo><i> janko@cs.bris.ac.uk</i>
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  Data: <searchLink fieldCode="JN" term="%22IEE+Proceedings+--+Vision%2C+Image+%26+Signal+Processing%22">IEE Proceedings -- Vision, Image & Signal Processing</searchLink>. Jun2006, Vol. 153 Issue 3, p305-312. 8p. 4 Diagrams, 3 Charts, 6 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Animal+behavior%22">Animal behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Wildlife+films%22">Wildlife films</searchLink><br /><searchLink fieldCode="DE" term="%22Tracking+%26+trailing%22">Tracking & trailing</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+detection%22">Signal detection</searchLink><br /><searchLink fieldCode="DE" term="%22Animal+species%22">Animal species</searchLink>
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  Label: Abstract
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  Data: An algorithm that categorises animal locomotive behaviour by combining detection and tracking of animal faces in wildlife videos is presented. As an example, the algorithm is applied to lion faces. The detection algorithm is based on a human face detection method, utilising Haar-like features and AdaBoost classifiers. The face tracking is implemented by applying a specific interest model that combines low-level feature tracking with the detection algorithm. By combining the two methods in a specific tracking model, reliable and temporally coherent detection/tracking of animal faces is achieved. The information generated by the tracker is used to automatically annotate the animal's locomotive behaviour. The annotation classes of locomotive processes for a given animal species are predefined by a large semantic taxonomy on wildlife domain. The experimental results are presented. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of IEE Proceedings -- Vision, Image & Signal Processing is the property of Institution of Engineering & Technology 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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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1049/ip-vis:20050052
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 8
        StartPage: 305
    Subjects:
      – SubjectFull: Animal behavior
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Wildlife films
        Type: general
      – SubjectFull: Tracking & trailing
        Type: general
      – SubjectFull: Signal detection
        Type: general
      – SubjectFull: Animal species
        Type: general
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      – TitleFull: Analysing animal behaviour in wildlife videos using face detection and tracking.
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              Text: Jun2006
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              Y: 2006
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