Automatic classification of flying bird species using computer vision techniques

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Atanbori, J., Duan, W., Murray, J., Appiah, K. ORCID: 0000-0002-9480-0679 and Dickinson, P., 2016. Automatic classification of flying bird species using computer vision techniques. Pattern Recognition Letters, 81, pp. 53-62. ISSN 0167-8655

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Official URL: http://dx.doi.org/10.1016/j.patrec.2015.08.015

Abstract

Bird populations are identified as important biodiversity indicators, so collecting reliable population data is important to ecologists and scientists. However, existing manual monitoring methods are labour-intensive, time-consuming, and potentially error prone. The aim of our work is to develop a reliable automated system, capable of classifying the species of individual birds, during flight, using video data. This is challenging, but appropriate for use in the field, since there is often a requirement to identify in flight, rather than while stationary. We present our work, which uses a new and rich set of appearance features for classification from video. We also introduce motion features including curvature and wing beat frequency. Combined with Normal Bayes classifier and a Support Vector Machine classifier, we present experimental evaluations of our appearance and motion features across a data set comprising 7 species. Using our appearance feature set alone we achieved a classification rate of 92% and 89% (using Normal Bayes and SVM classifiers respectively) which significantly outperforms a recent comparable state-of-the-art system. Using motion features alone we achieved a lower-classification rate, but motivate our on-going work which we seeks to combine these appearance and motion feature to achieve even more robust classification.

Item Type:

Journal article

Publication Title:

Pattern Recognition Letters

Creators:

Atanbori, J., Duan, W., Murray, J., Appiah, K. and Dickinson, P.

Publisher:

Elsevier

Date:

1 October 2016

Volume:

ISSN: