Automatic classification of flying bird species using computer vision techniques

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

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.

Citation

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. https://doi.org/10.1016/j.patrec.2015.08.015

• 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
• DOI: https://doi.org/10.1016/j.patrec.2015.08.015
• Keywords: Signal Processing; Software; Artificial Intelligence; Computer Vision and Pattern Recognition
• Public URL: https://hull-repository.worktribe.com/output/799518
• Publisher URL: http://eprints.lincoln.ac.uk/18588/
• 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: http://dx.doi.org/10.1016/j.patrec.2015.08.015; Content Type: article; Copyright: © 2015 Elsevier B.V. All rights reserved.