Deep Learning for Leopard Individual Identification: An Adaptive Angular Margin Approach

• URL: http://arxiv.org/abs/2411.01962v1
• Date: Mon, 04 Nov 2024 10:38:33 GMT
• Title: Deep Learning for Leopard Individual Identification: An Adaptive Angular Margin Approach
• Authors: David Colomer Matachana,
• Abstract summary: This paper introduces a deep learning framework to distinguish between individual leopards based on their unique spot patterns. I propose a preprocessing pipeline that combines RGB channels with an edge detection channel to underscore the critical features learned by the model.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Accurate identification of individual leopards across camera trap images is critical for population monitoring and ecological studies. This paper introduces a deep learning framework to distinguish between individual leopards based on their unique spot patterns. This approach employs a novel adaptive angular margin method in the form of a modified CosFace architecture. In addition, I propose a preprocessing pipeline that combines RGB channels with an edge detection channel to underscore the critical features learned by the model. This approach significantly outperforms the Triplet Network baseline, achieving a Dynamic Top-5 Average Precision of 0.8814 and a Top-5 Rank Match Detection of 0.9533, demonstrating its potential for open-set learning in wildlife identification. While not surpassing the performance of the SIFT-based Hotspotter algorithm, this method represents a substantial advancement in applying deep learning to patterned wildlife identification. This research contributes to the field of computer vision and provides a valuable tool for biologists aiming to study and protect leopard populations. It also serves as a stepping stone for applying the power of deep learning in Capture-Recapture studies for other patterned species.

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