Metadata Augmented Deep Neural Networks for Wild Animal Classification
Tøn, Aslak and Ahmed, Ammar and Imran, Ali Shariq and Ullah, Mohib and Azad, R. Muhammad Atif (2024) Metadata Augmented Deep Neural Networks for Wild Animal Classification. Ecological Informatics, 83. ISSN 1574-9541
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Abstract
Camera trap imagery has become an invaluable asset in contemporary wildlife surveillance, enabling researchers to observe and investigate the behaviors of wild animals. While existing methods rely solely on image data for classification, this may not suffice in cases of suboptimal animal angles, lighting, or image quality. This study introduces a novel approach that enhances wild animal classification by combining specific metadata (temperature, location, time, etc) with image data. Using a dataset focused on the Norwegian climate, our models show an accuracy increase from 98.4% to 98.9% compared to existing methods. Notably, our approach also achieves high accuracy with metadata-only classification, highlighting its potential to reduce reliance on image quality. This work paves the way for integrated systems that advance wildlife classification technology.
| Item Type: | Article | ||||||
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| Identification Number: | https://doi.org/10.1016/j.ecoinf.2024.102805 | ||||||
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| Uncontrolled Keywords: | Wild animal detection, Wild animal classification, deep learning data fusion | ||||||
| Subjects: | CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science | ||||||
| Divisions: | Faculty of Computing, Engineering and the Built Environment > College of Computing | ||||||
| Depositing User: | Gemma Tonks | ||||||
| Date Deposited: | 17 Sep 2024 10:50 | ||||||
| Last Modified: | 17 Sep 2024 10:50 | ||||||
| URI: | https://www.open-access.bcu.ac.uk/id/eprint/15850 |
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