ArticleAnimals : an open access journal from MDPI2023
PolarBearVidID: A Video-Based Re-Identification Benchmark Dataset for Polar Bears.
Article in Animals : an open access journal from MDPI, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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.
Who cites it
6 citing papers in PubMed.
- HST-former: hierarchical spatio-temporal aggregation for video-based animal re-identification.Scientific reports · 2026Article
- CzechLynx: A Dataset for Individual Identification and Pose Estimation of the Eurasian Lynx.Scientific data · 2026Article
- Beyond the Camera Trap: A Systematic Review of Computing Technology Used to Monitor and Interact with (More) Varied Taxa in Zoos and Aquariums.Animals : an open access journal from MDPI · 2025Review
- Animal re-identification in video through track clustering.Pattern analysis and applications : PAA · 2025Article
- Towards Automated Chicken Monitoring: Dataset and Machine Learning Methods for Visual, Noninvasive Reidentification.Animals : an open access journal from MDPI · 2024Article
- Dolphin-WET-Development of a Welfare Evaluation Tool for Bottlenose Dolphins (Animals : an open access journal from MDPI · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automated monitoring systems have become increasingly important for zoological institutions in the study of their animals' behavior. One crucial processing step for such a system is the re-identification of individuals when using multiple cameras. Deep learning approaches have become the standard methodology for this task. Especially video-based methods promise to achieve a good performance in re-identification, as they can leverage the movement of an animal as an additional feature. This is especially important for applications in zoos, where one has to overcome specific challenges such as changing lighting conditions, occlusions or low image resolutions. However, large amounts of labeled data are needed to train such a deep learning model. We provide an extensively annotated dataset including 13 individual polar bears shown in 1431 sequences, which is an equivalent of 138,363 images.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.