Identification of previously unseen Asian elephants using visual data and semi-supervised learning

Weerasinghe, Gagana, Karunanayaka, Kasun, Kumarasinghe, Prabhash, Perera, Dushani, Trenado, Carlos, De Zoysa, Kasun and Keppitiyagama, Chamath 2023. Identification of previously unseen Asian elephants using visual data and semi-supervised learning. Presented at: 22nd International Conference on Advances in ICT for Emerging Regions, 30 November 2022 - 01 December 2022. Proceedings 2022 22nd International Conference on Advances in ICT for Emerging Regions (ICTer). IEEE, 10.1109/ICTer58063.2022.10024068

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Abstract

This paper presents a novel method to identify unseen Asian elephants that are not previously captured or identified in available data sets and re-identify previously seen Asian elephants using images of elephant ears, leveraging a semi-supervised learning approach. Ear patterns of unseen elephants are learnt for future re-identification. To aid our process, elephant ear patterns are used as a biomarker to uniquely identify individual Asian elephant, each of which is attached a descriptor. The main challenge is to learn and use a clustering technique to identify new classes (i.e., elephants) in unlabelled elephant ear image sets and leveraging this data in verifying the labelled images. This study proposes a systematic approach to address the problem to uniquely identify elephants, where we developed: (a) a self-supervised learning approach for training the representation of labelled and unlabelled image data to avoid unWanted, bias labelled data, (b) rank statistics for transferring the models’ knowledge of the labelled classes when clustering the unlabelled images, and, (c) improving the identification accuracy of both the classification and clustering algorithms by introducing a optimization problem when training with the data representation on the labelled and unlabelled image data sets. This approach was evaluated on seen (labelled) and unseen (unlabelled) elephants, where we achieved a significant accuracy of 86.89% with an NMI (Normalized Mutual Information) score of 0.9132 on identifying seen elephants. Similarly, an accuracy of 54.29% with an NMI score of 0.6250 was achieved on identifying unseen elephants from the unlabelled Asian elephant ear image data set. Findings of this research provides the ability to accurately identify elephants without having expert knowledge on the field. Our method can be used to uniquely identify elephants from their herds and then use it to track their travel patterns Which is greatly applicable in understanding the social organization of elephant herds, individual behavioural patterns, and estimating demographic parameters as a measure to reducing the human-elephant conflict in Sri Lanka.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	IEEE
ISBN:	979-8-3503-4613-8
Date of First Compliant Deposit:	16 February 2024
Last Modified:	22 Apr 2024 01:30
URI:	https://orca.cardiff.ac.uk/id/eprint/166348

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