Persistent animal identification leveraging non-visual markers.

Michael P J Camilleri, Li Zhang, Rasneer S Bains, Andrew Zisserman, Christopher K I Williams

Open access · hybridAbstract read

In one paragraph

Article in Machine vision and applications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it

0cells of the map it votes in

2citing papers in PubMed

0.5field-weighted citation impact, top 36% of its field

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 4 citations in OpenAlex.

A real-time, multi-animal model for automatic face detection and identification of freely moving common marmosets based on YOLOv8 algorithms.eLife · 2026
Article
Of Mice and Mates: Automated Classification and Modelling of Mouse Behaviour in Groups Using a Single Model Across Cages.International journal of computer vision · 2024
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 4 institutions in 2 countries.

Michael P J CamilleriSchool of Informatics, University of Edinburgh, Edinburgh, UK.ORCID 0000-0002-8619-3231

Li ZhangSchool of Data Science, Fudan University, Shanghai, China.

Rasneer S BainsMary Lyon Centre at MRC Harwell, Oxfordshire, UK.

Andrew ZissermanDepartment of Engineering Science, University of Oxford, Oxford, UK.

Christopher K I WilliamsSchool of Informatics, University of Edinburgh, Edinburgh, UK.

University of Edinburgh · GBFudan University · CNMary Lyon Centre at MRC Harwell · GBUniversity of Oxford · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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

Indexed as

Group-housed miceLinear programmingLocalisationObject identification

Identifiers

PMID37457592

DOI10.1007/s00138-023-01414-1

PMCPMC10345053

OpenAlexW4383474072

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.