2026-04-23T00:07:27Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/75412025-01-19T23:42:07Zcom_10259_4219com_10259_5086com_10259_2604col_10259_4220
Kuncheva, Ludmila I. .
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600
Garrido Labrador, José Luis
745
600
0000-0002-3441-4223
Ramos Pérez, Ismael
751
600
0000-0002-0330-1605
Hennessey, Samuel L.
4d2639df-71b9-4a45-85e3-ab4da265de74
Rodríguez Diez, Juan José
477
600
0000-0002-3291-2739
2023-03-14T11:13:26Z
2023-03-14T11:13:26Z
2023-05
1574-9541
http://hdl.handle.net/10259/7541
10.1016/j.ecoinf.2023.101994
In the face of the global concern about climate change and endangered ecosystems, monitoring individual animals is of paramount importance. Computer vision methods for animal recognition and re-identification from video or image collections are a modern alternative to more traditional but intrusive methods such as tagging or branding. While there are many studies reporting results on various animal re-identification databases, there is a notable lack of comparative studies between different classification methods. In this paper we offer a comparison of 25 classification methods including linear, non-linear and ensemble models, as well as deep learning networks. Since the animal databases are vastly different in characteristics and difficulty, we propose an experimental protocol that can be applied to a chosen data collections. We use a publicly available database of five video clips, each containing multiple identities (9 to 27), where the animals are typically present as a group in each video frame. Our experiment involves five data representations: colour, shape, texture, and two feature spaces extracted by deep learning. In our experiments, simpler models (linear classifiers) and just colour feature space gave the best classification accuracy, demonstrating the importance of running a comparative study before resorting to complex, time-consuming, and potentially less robust methods.
This work is supported by the UKRI Centre for Doctoral Training in Artificial Intelligence, Machine Learning and Advanced Computing (AIMLAC), funded by grant EP/S023992/1. This work is also supported by the Junta de Castilla León under project BU055P20 (JCyL/FEDER, UE), and the Ministry of Science and Innovation under project PID2020-119894 GB-I00 co-financed through European Union FEDER funds. J.L. Garrido-Labrador is supported through Consejería de Educación of the Junta de Castilla y León and the European Social Fund through a pre-doctoral grant (EDU/875/2021). I. Ramos-Perez is supported by the predoctoral grant (BDNS 510149) awarded by the Universidad de Burgos, Spain. J.J. Rodríguez was supported by mobility grant PRX21/00638 of the Spanish Ministry of Universities.
application/pdf
eng
Elsevier
Ecological Informatics. 2023, V. 74, 101994
https://doi.org/10.1016/j.ecoinf.2023.101994
info:eu-repo/grantAgreement/UKRI//EP%2FS023992%2F1/GB/
info:eu-repo/grantAgreement/Junta de Castilla y León//BU055P20//Métodos y Aplicaciones Industriales del Aprendizaje Semisupervisado
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-119894GB-I00/ES/APRENDIZAJE AUTOMATICO CON DATOS ESCASAMENTE ETIQUETADOS PARA LA INDUSTRIA 4.0/
info:eu-repo/grantAgreement/MIU//PRX21%2F00638/ES/
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
Animal re-identification
Computer vision
Classification
Convolutional networks
Comparative study
Informática
Biología
Computer science
Biology
An experiment on animal re-identification from video
info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
Ecological Informatics
74
101994
THUMBNAIL
Kuncheva-ei_2023.pdf.jpg
Kuncheva-ei_2023.pdf.jpg
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Kuncheva-ei_2023.pdf
Kuncheva-ei_2023.pdf
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oai:riubu.ubu.es:10259/7541
2025-01-20 00:42:07.9
Repositorio Institucional de la Universidad de Burgos
bubrep@ubu.es
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