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Classification of bird species from video using appearance and motion features

Atanbori, John; Duan, Wenting; Shaw, Edward; Appiah, Kofi; Dickinson, Patrick


Wenting Duan

Edward Shaw

Kofi Appiah

Patrick Dickinson


The monitoring of bird populations can provide important information on the state of sensitive ecosystems; however, the manual collection of reliable population data is labour-intensive, time-consuming, and potentially error prone. Automated monitoring using computer vision is therefore an attractive proposition, which could facilitate the collection of detailed data on a much larger scale than is currently possible.

A number of existing algorithms are able to classify bird species from individual high quality detailed images often using manual inputs (such as a priori parts labelling). However, deployment in the field necessitates fully automated in-flight classification, which remains an open challenge due to poor image quality, high and rapid variation in pose, and similar appearance of some species. We address this as a fine-grained classification problem, and have collected a video dataset of thirteen bird classes (ten species and another with three colour variants) for training and evaluation. We present our proposed algorithm, which selects effective features from a large pool of appearance and motion features. We compare our method to others which use appearance features only, including image classification using state-of-the-art Deep Convolutional Neural Networks (CNNs). Using our algorithm we achieved an 90% correct classification rate, and we also show that using effectively selected motion and appearance features together can produce results which outperform state-of-the-art single image classifiers. We also show that the most significant motion features improve correct classification rates by 7% compared to using appearance features alone.


Atanbori, J., Duan, W., Shaw, E., Appiah, K., & Dickinson, P. (2018). Classification of bird species from video using appearance and motion features. Ecological informatics, 48, 12-23.

Journal Article Type Article
Acceptance Date Jul 11, 2018
Online Publication Date Jul 18, 2018
Publication Date 2018-11
Deposit Date Nov 13, 2019
Publicly Available Date Nov 13, 2019
Journal Ecological Informatics
Print ISSN 1574-9541
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 48
Pages 12-23
Keywords Appearance features; Motion features; Feature extraction; Feature selection; Bird species classification; Fine-grained classification
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Additional Information This article is maintained by: Elsevier; Article Title: Classification of bird species from video using appearance and motion features; Journal Title: Ecological Informatics; CrossRef DOI link to publisher maintained version:; Content Type: article; Copyright: © 2018 Elsevier B.V. All rights reserved.


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