A computer vision approach to classification of birds in flight from video sequences
Atanbori, John; Duan, Wenting; Murray, John; Appiah, Kofi; Dickinson, Patrick
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%.
Atanbori, J., Duan, W., Murray, J., Appiah, K., & Dickinson, P. (2015). A computer vision approach to classification of birds in flight from video sequences. In Proceedings of the Machine Vision of Animals and their Behaviour (MVAB) (3.1-3.9). https://doi.org/10.5244/c.29.mvab.3
|Conference Name||Machine Vision of Animals and their Behaviour Workshop 2015|
|Conference Location||Swansea, UK|
|Start Date||Sep 7, 2015|
|End Date||Sep 10, 2015|
|Acceptance Date||Aug 18, 2015|
|Publication Date||Sep 7, 2015|
|Deposit Date||Oct 25, 2018|
|Publicly Available Date||Oct 31, 2018|
|Book Title||Proceedings of the Machine Vision of Animals and their Behaviour (MVAB)|
© 2015. The copyright of this document resides with its authors.
It may be distributed unchanged freely in print or electronic forms.
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