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Title: Species-Agnostic Patterned Animal Re-identification by Aggregating Deep Local Features
Abstract Access to large image volumes through camera traps and crowdsourcing provides novel possibilities for animal monitoring and conservation. It calls for automatic methods for analysis, in particular, when re-identifying individual animals from the images. Most existing re-identification methods rely on either hand-crafted local features or end-to-end learning of fur pattern similarity. The former does not need labeled training data, while the latter, although very data-hungry typically outperforms the former when enough training data is available. We propose a novel re-identification pipeline that combines the strengths of both approaches by utilizing modern learnable local features and feature aggregation. This creates representative pattern feature embeddings that provide high re-identification accuracy while allowing us to apply the method to small datasets by using pre-trained feature descriptors. We report a comprehensive comparison of different modern local features and demonstrate the advantages of the proposed pipeline on two very different species.  more » « less
Award ID(s):
2118240
PAR ID:
10530287
Author(s) / Creator(s):
; ; ; ; ; ;
Publisher / Repository:
Springer
Date Published:
Journal Name:
International Journal of Computer Vision
ISSN:
0920-5691
Subject(s) / Keyword(s):
Computer vision Image processing Animal biometrics Re-identification Ringed seals Convolutional neural networks imageomics
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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