Matching individual Ladoga ringed seals across short-term image sequences

Automated wildlife reidentification has attracted increasing attention in recent years as it provides a non-invasive tool to identify and to track individual wild animals over time. In this paper, the first steps are taken towards the automatic photo-identification of the Ladoga ringed seals (Pusa h...

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Published in:Mammalian Biology
Main Authors: Nepovinnykh, Ekaterina, Chelak, Ilia, Lushpanov, Andrei, Eerola, Tuomas, Kälviäinen, Heikki, Chirkova, Olga
Other Authors: Lappeenrannan-Lahden teknillinen yliopisto LUT, Lappeenranta-Lahti University of Technology LUT, fi=School of Engineering Science|en=School of Engineering Science|
Format: Article in Journal/Newspaper
Language:English
Published: Springer Nature 2022
Subjects:
Online Access:https://lutpub.lut.fi/handle/10024/164700
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record_format openpolar
spelling ftlappeenranta:oai:lutpub.lut.fi:10024/164700 2023-05-15T18:03:47+02:00 Matching individual Ladoga ringed seals across short-term image sequences Nepovinnykh, Ekaterina Chelak, Ilia Lushpanov, Andrei Eerola, Tuomas Kälviäinen, Heikki Chirkova, Olga Lappeenrannan-Lahden teknillinen yliopisto LUT Lappeenranta-Lahti University of Technology LUT fi=School of Engineering Science|en=School of Engineering Science| 2022-04-08 16 fulltext 1-16 https://lutpub.lut.fi/handle/10024/164700 eng eng Springer Nature 1618-1476 1616-5047 Mammalian Biology https://link.springer.com/article/10.1007/s42991-022-00229-3 https://doi.org/10.1007/s42991-022-00229-3 Nepovinnykh, E., Chelak, I., Lushpanov, A. et al. Matching individual Ladoga ringed seals across short-term image sequences. Mamm Biol (2022). https://doi.org/10.1007/s42991-022-00229-3 https://lutpub.lut.fi/handle/10024/164700 URN:NBN:fi-fe2022091358854 CC BY 4.0 openAccess © The Author(s) 2022 CC-BY Ladoga ringed seal animal re-identification photo-identification instance segmentation convolutional neural networks article publishedVersion 2022 ftlappeenranta https://doi.org/10.1007/s42991-022-00229-3 2022-09-28T22:57:22Z Automated wildlife reidentification has attracted increasing attention in recent years as it provides a non-invasive tool to identify and to track individual wild animals over time. In this paper, the first steps are taken towards the automatic photo-identification of the Ladoga ringed seals (Pusa hispida ladogensis). A method is proposed that takes a sequence of images, each containing multiple individuals as the input, and produces cropped images of seals grouped based on one certain individual per group. The method starts by detecting each seal from the images and proceeds to matching the individual seals between the images. It is shown that high grouping accuracy can be obtained with a general-purpose image retrieval method on an image sequence taken from the same location within a relatively short period of time. Each resulting group contains multiple images of one individual with slightly different variations, for example, in pose and illumination. Utilizing these images simultaneously provides more information for the individual re-identification compared to the traditional approach, i.e., which utilizes just one image at a time. It is further demonstrated that a convolutional neural network based method can be used to extract the unique pelage patterns of the seals despite the low contrast. Finally, a method is proposed and experiments with the novel Ladoga ringed seals data are carried out to provide a proof-of-concept for the individual re-identification. Publishers version Article in Journal/Newspaper Pusa hispida ringed seal LUTPub (LUT University) Mammalian Biology 102 3 935 950
institution Open Polar
collection LUTPub (LUT University)
op_collection_id ftlappeenranta
language English
topic Ladoga ringed seal
animal re-identification
photo-identification
instance segmentation
convolutional neural networks
spellingShingle Ladoga ringed seal
animal re-identification
photo-identification
instance segmentation
convolutional neural networks
Nepovinnykh, Ekaterina
Chelak, Ilia
Lushpanov, Andrei
Eerola, Tuomas
Kälviäinen, Heikki
Chirkova, Olga
Matching individual Ladoga ringed seals across short-term image sequences
topic_facet Ladoga ringed seal
animal re-identification
photo-identification
instance segmentation
convolutional neural networks
description Automated wildlife reidentification has attracted increasing attention in recent years as it provides a non-invasive tool to identify and to track individual wild animals over time. In this paper, the first steps are taken towards the automatic photo-identification of the Ladoga ringed seals (Pusa hispida ladogensis). A method is proposed that takes a sequence of images, each containing multiple individuals as the input, and produces cropped images of seals grouped based on one certain individual per group. The method starts by detecting each seal from the images and proceeds to matching the individual seals between the images. It is shown that high grouping accuracy can be obtained with a general-purpose image retrieval method on an image sequence taken from the same location within a relatively short period of time. Each resulting group contains multiple images of one individual with slightly different variations, for example, in pose and illumination. Utilizing these images simultaneously provides more information for the individual re-identification compared to the traditional approach, i.e., which utilizes just one image at a time. It is further demonstrated that a convolutional neural network based method can be used to extract the unique pelage patterns of the seals despite the low contrast. Finally, a method is proposed and experiments with the novel Ladoga ringed seals data are carried out to provide a proof-of-concept for the individual re-identification. Publishers version
author2 Lappeenrannan-Lahden teknillinen yliopisto LUT
Lappeenranta-Lahti University of Technology LUT
fi=School of Engineering Science|en=School of Engineering Science|
format Article in Journal/Newspaper
author Nepovinnykh, Ekaterina
Chelak, Ilia
Lushpanov, Andrei
Eerola, Tuomas
Kälviäinen, Heikki
Chirkova, Olga
author_facet Nepovinnykh, Ekaterina
Chelak, Ilia
Lushpanov, Andrei
Eerola, Tuomas
Kälviäinen, Heikki
Chirkova, Olga
author_sort Nepovinnykh, Ekaterina
title Matching individual Ladoga ringed seals across short-term image sequences
title_short Matching individual Ladoga ringed seals across short-term image sequences
title_full Matching individual Ladoga ringed seals across short-term image sequences
title_fullStr Matching individual Ladoga ringed seals across short-term image sequences
title_full_unstemmed Matching individual Ladoga ringed seals across short-term image sequences
title_sort matching individual ladoga ringed seals across short-term image sequences
publisher Springer Nature
publishDate 2022
url https://lutpub.lut.fi/handle/10024/164700
genre Pusa hispida
ringed seal
genre_facet Pusa hispida
ringed seal
op_relation 1618-1476
1616-5047
Mammalian Biology
https://link.springer.com/article/10.1007/s42991-022-00229-3
https://doi.org/10.1007/s42991-022-00229-3
Nepovinnykh, E., Chelak, I., Lushpanov, A. et al. Matching individual Ladoga ringed seals across short-term image sequences. Mamm Biol (2022). https://doi.org/10.1007/s42991-022-00229-3
https://lutpub.lut.fi/handle/10024/164700
URN:NBN:fi-fe2022091358854
op_rights CC BY 4.0
openAccess
© The Author(s) 2022
op_rightsnorm CC-BY
op_doi https://doi.org/10.1007/s42991-022-00229-3
container_title Mammalian Biology
container_volume 102
container_issue 3
container_start_page 935
op_container_end_page 950
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