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 (2018), 12-23.
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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 a 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.
Item Type: | Article |
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Research Institute, Centre or Group - Does NOT include content added after October 2018: | Cultural Communication and Computing Research Institute > Communication and Computing Research Centre |
Departments - Does NOT include content added after October 2018: | Faculty of Science, Technology and Arts > Department of Computing |
Identification Number: | https://doi.org/10.1016/j.ecoinf.2018.07.005 |
Page Range: | 12-23 |
Depositing User: | Kofi Appiah |
Date Deposited: | 23 Jul 2018 11:40 |
Last Modified: | 18 Mar 2021 07:23 |
URI: | https://shura.shu.ac.uk/id/eprint/22060 |
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