Persistent animal identification leveraging non-visual markers.

Group-housed mice Linear programming Localisation Object identification

Journal

Machine vision and applications
ISSN: 0932-8092
Titre abrégé: Mach Vis Appl
Pays: United States
ID NLM: 101562623

Informations de publication

Date de publication:
2023
Historique:
received: 01 03 2022
revised: 29 05 2023
accepted: 12 06 2023
medline: 17 7 2023
pubmed: 17 7 2023
entrez: 17 7 2023
Statut: ppublish

Résumé

Our objective is to locate and provide a unique identifier for each mouse in a cluttered home-cage environment through time, as a precursor to automated behaviour recognition for biological research. This is a very challenging problem due to (i) the lack of distinguishing visual features for each mouse, and (ii) the close confines of the scene with constant occlusion, making standard visual tracking approaches unusable. However, a coarse estimate of each mouse's location is available from a unique RFID implant, so there is the potential to optimally combine information from (weak) tracking with coarse information on identity. To achieve our objective, we make the following key contributions: (a) the formulation of the

Identifiants

pubmed: 37457592
doi: 10.1007/s00138-023-01414-1
pii: 1414
pmc: PMC10345053
doi:

Types de publication

Journal Article

Langues

eng

Pagination

68

Informations de copyright

© The Author(s) 2023.

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Auteurs

Michael P J Camilleri (MPJ)

School of Informatics, University of Edinburgh, Edinburgh, UK.

Li Zhang (L)

School of Data Science, Fudan University, Shanghai, China.

Rasneer S Bains (RS)

Mary Lyon Centre at MRC Harwell, Oxfordshire, UK.

Andrew Zisserman (A)

Department of Engineering Science, University of Oxford, Oxford, UK.

Christopher K I Williams (CKI)

School of Informatics, University of Edinburgh, Edinburgh, UK.

Classifications MeSH