Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/4155
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dc.contributor.advisorHarrison, DJen
dc.contributor.advisorSouthee, Den
dc.contributor.authorLazarevic, Ljubica-
dc.date.accessioned2010-03-05T14:33:34Z-
dc.date.available2010-03-05T14:33:34Z-
dc.date.issued2010-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/4155-
dc.descriptionThis thesis was submitted for the degree of Engineering Doctorate and awarded by Brunel University.-
dc.description.abstractBirds are a key environmental asset and this is recognised through comprehensive legislation and policy ensuring their protection and conservation. Many species are active at night and surveys are required to understand the implications of proposed developments such as towers and reduce possible conflicts with these structures. Night vision devices are commonly used in nocturnal surveys, either to scope an area for bird numbers and activity, or in remotely sensing an area to determine potential risk. This thesis explores some practical and theoretical approaches that can improve the accuracy, confidence and efficiency of nocturnal bird surveillance. As image intensifiers and thermal imagers have operational differences, each device has associated strengths and limitations. Empirical work established that image intensifiers are best used for species identification of birds against the ground or vegetation. Thermal imagers perform best in detection tasks and monitoring bird airspace usage. The typically used approach of viewing bird survey video from remote sensing in its entirety is a slow, inaccurate and inefficient approach. Accuracy can be significantly improved by viewing the survey video at half the playback speed. Motion detection efficiency and accuracy can be greatly improved through the use of adaptive background subtraction and cumulative image differencing. An experienced ornithologist uses bird flight style and wing oscillations to identify bird species. Changes in wing oscillations can be represented in a single inter-frame similarity matrix through area-based differencing. Bird species classification can then be automated using singular value decomposition to reduce the matrices to one-dimensional vectors for training a feed-forward neural network.en
dc.language.isoenen
dc.publisherBrunel University School of Engineering and Design PhD Theses-
dc.relation.urihttp://bura.brunel.ac.uk/bitstream/2438/4155/1/FulltextThesis.pdf-
dc.subjectNocturnal bird surveysen
dc.subjectSurveillanceen
dc.subjectInfrareden
dc.subjectBird classificationen
dc.subjectCyclic motion detectionen
dc.subjectSimilarity matrixen
dc.subjectSingular value decompositionen
dc.subjectTime lapseen
dc.subjectMotion detectionen
dc.titleImproving the efficiency and accuracy of nocturnal bird Surveys through equipment selection and partial automationen
dc.typeThesisen
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Design
Dept of Design Theses

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