ATANBORI, J., DUAN, W., MURRAY, J., APPIAH, K. and DICKINSON, P., 2015. A computer vision approach to classification of birds in flight from video sequences. In: T. AMARAL, S. MATTHEWS, T. PLÖTZ, S. MCKENNA and R. FISHER, eds., Proceedings of the Machine Vision of Animals and their Behaviour (MVAB 2015) Workshop, Swansea, 10 September 2015. BMVA Press.
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Bird populations are an important bio-indicator; so collecting reliable data is useful for ecologists helping conserve and manage fragile ecosystems. However, existing manual monitoring methods are labour-intensive, time-consuming, and error-prone. The aim of our work is to develop a reliable system, capable of automatically classifying individual bird species in flight from videos. This is challenging, but appropriate for use in the field, since there is often a requirement to identify in flight, rather than when stationary. We present our work in progress, which uses combined appearance and motion features to classify and present experimental results across seven species using Normal Bayes classifier with majority voting and achieving a classification rate of 86%.
|Item Type:||Chapter in book|
|Creators:||Atanbori, J., Duan, W., Murray, J., Appiah, K. and Dickinson, P.|
|Divisions:||Schools > School of Science and Technology|
|Depositing User:||EPrints Services|
|Date Added:||09 Oct 2015 10:07|
|Last Modified:||19 Oct 2015 14:27|
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