RT info:eu-repo/semantics/article
T1 An experiment on animal re-identification from video
A1 Kuncheva, Ludmila I. .
A1 Garrido Labrador, José Luis
A1 Ramos Pérez, Ismael
A1 Hennessey, Samuel L.
A1 Rodríguez Diez, Juan José
K1 Animal re-identification
K1 Computer vision
K1 Classification
K1 Convolutional networks
K1 Comparative study
K1 Informática
K1 Computer science
K1 Biología
K1 Biology
AB 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.
PB Elsevier
SN 1574-9541
YR 2023
FD 2023-05
LK http://hdl.handle.net/10259/7541
UL http://hdl.handle.net/10259/7541
LA eng
NO 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.
DS Repositorio Institucional de la Universidad de Burgos
RD 28-abr-2024