Atanbori, John, Duan, Wenting, Shaw, Edward , Appiah, Kofi and Dickinson, Patrick (2018) Classification of bird species from video using appearance and motion features. Ecological Informatics, 48 . pp. 12-23. ISSN 1574-9541
Full content URL: http://doi.org/10.1016/j.ecoinf.2018.07.005
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atanbori-ecoinf-2018.pdf - Whole Document Available under License Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International. 1MB |
Item Type: | Article |
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Item Status: | Live Archive |
Abstract
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.
Keywords: | Computer vision, Bird specied recognition |
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Subjects: | G Mathematical and Computer Sciences > G400 Computer Science |
Divisions: | College of Science > School of Computer Science |
ID Code: | 32791 |
Deposited On: | 23 Jul 2018 09:10 |
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