A Computer Vision Approach to Classification of Birds in Flight from Video Sequences

John Atanbori, Wenting Duan, John Murray, Kofi Appiah and Patrick Dickinson

Abstract

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%.

Session

Workshop: Machine Vision of Animals and their Behaviour (MVAB 2015)

Files

Paper (PDF, 371K)

DOI

10.5244/C.29.MVAB.3
https://dx.doi.org/10.5244/C.29.MVAB.3

Citation

Bibtex

@inproceedings{MVAB2015_3,
	title={A Computer Vision Approach to Classification of Birds in Flight from Video Sequences},
	author={John Atanbori and Wenting Duan and John Murray and Kofi Appiah and Patrick Dickinson},
	year={2015},
	month={September},
	pages={3.1-3.9},
	articleno={3},
	numpages={9},
	booktitle={Proceedings of the Machine Vision of Animals and their Behaviour (MVAB)},
	publisher={BMVA Press},
	editor={T. Amaral, S. Matthews, T. Plötz, S. McKenna, and R. Fisher},
	doi={10.5244/C.29.MVAB.3},
	isbn={1-901725-57-X},
	url={https://dx.doi.org/10.5244/C.29.MVAB.3}
}