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Automatic classification of flying bird species using computer vision techniques

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


John Atanbori

Wenting Duan

John Murray

Kofi Appiah

Patrick Dickinson


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.


Atanbori, J., Duan, W., Murray, J., Appiah, K., & Dickinson, P. (2016). Automatic classification of flying bird species using computer vision techniques. Pattern recognition letters, 81, 53-62.

Journal Article Type Article
Acceptance Date Aug 14, 2015
Online Publication Date Sep 3, 2015
Publication Date Oct 1, 2016
Deposit Date May 3, 2018
Journal Pattern Recognition Letters
Print ISSN 0167-8655
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 81
Pages 53-62
Keywords Signal Processing; Software; Artificial Intelligence; Computer Vision and Pattern Recognition
Public URL
Publisher URL
Additional Information This article is maintained by: Elsevier; Article Title: Automatic classification of flying bird species using computer vision techniques; Journal Title: Pattern Recognition Letters; CrossRef DOI link to publisher maintained version:; Content Type: article; Copyright: © 2015 Elsevier B.V. All rights reserved.