Evidence map›Paper›PMID 35327089›Full record

ArticleAnimals : an open access journal from MDPI2022

Automated Video-Based Analysis Framework for Behavior Monitoring of Individual Animals in Zoos Using Deep Learning-A Study on Polar Bears.

Matthias Zuerl, Philip Stoll, Ingrid Brehm, René Raab, Dario Zanca, Samira Kabri, Johanna Happold, Heiko Nille, Katharina Prechtel, Sophie Wuensch and 4 more

Abstract read
In one paragraph

Article in Animals : an open access journal from MDPI, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
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

14 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Captive Animal Behavior Study by Video Analysis.Sensors (Basel, Switzerland) · 2023
    Article
  9. Article
  10. Article
  11. Article
  12. Development of a Slow Loris Computer Vision Detection Model.Animals : an open access journal from MDPI · 2022
    Article
  13. Study on Poultry Pose Estimation Based on Multi-Parts Detection.Animals : an open access journal from MDPI · 2022
    Article
  14. Review
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

14 authors.

Matthias ZuerlMachine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, Germany.ORCID 0000-0002-9678-166X
Philip StollMachine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, Germany.ORCID 0000-0002-4763-0427
Ingrid BrehmAnimal Physiology, Department Biology, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany.
René RaabMachine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, Germany.ORCID 0000-0003-2035-3332
Dario ZancaMachine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, Germany.ORCID 0000-0001-5886-0597
Samira KabriMachine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, Germany.
Johanna HappoldMachine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, Germany.
Heiko NilleMachine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, Germany.
Katharina PrechtelAnimal Physiology, Department Biology, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany.
Sophie WuenschAnimal Physiology, Department Biology, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany.
Marie KrauseAnimal Physiology, Department Biology, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany.
Stefan SeegererComputing Education Research Group, Department of Mathematics and Computer Science, Freie Universität Berlin, 14195 Berlin, Germany.
Lorenzo von FersenNuremberg Zoo, 90480 Nuremberg, Germany.ORCID 0000-0002-3451-0530
Bjoern EskofierMachine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, Germany.ORCID 0000-0002-0417-0336

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The monitoring of animals under human care is a crucial tool for biologists and zookeepers to keep track of the animals' physical and psychological health. Additionally, it enables the analysis of observed behavioral changes and helps to unravel underlying reasons. Enhancing our understanding of animals ensures and improves ex situ animal welfare as well as in situ conservation. However, traditional observation methods are time- and labor-intensive, as they require experts to observe the animals on-site during long and repeated sessions and manually score their behavior. Therefore, the development of automated observation systems would greatly benefit researchers and practitioners in this domain. We propose an automated framework for basic behavior monitoring of individual animals under human care. Raw video data are processed to continuously determine the position of the individuals within the enclosure. The trajectories describing their travel patterns are presented, along with fundamental analysis, through a graphical user interface (GUI). We evaluate the performance of the framework on captive polar bears (

Indexed as

animal behavioranimal monitoringanimal welfarebehavior observationdeep learningobject detectionUrsus maritimus

Identifiers

PMID35327089
PMCPMC8944680

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.