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Title: Improving the efficiency and accuracy of nocturnal bird Surveys through equipment selection and partial automation
Authors: Lazarevic, Ljubica
Advisors: Harrison, DJ
Southee, D
Keywords: Nocturnal bird surveys;Surveillance;Infrared;Bird classification;Cyclic motion detection;Similarity matrix;Singular value decomposition;Time lapse;Motion detection
Issue Date: 2010
Publisher: Brunel University School of Engineering and Design PhD Theses
Abstract: Birds 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.
Description: This thesis was submitted for the degree of Engineering Doctorate and awarded by Brunel University.
Appears in Collections:Brunel University Theses
Brunel Design School Theses

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