Options
3D Zebrafish Tracking Dataset
Citation Link: https://doi.org/10.15480/882.17350
Type
Video
Version
1.0.0
Date Issued
2026-07-02
Data Collector
Language
English
TORE-DOI
Abstract
This dataset contains synchronized stereo-camera RGB recordings of zebrafish swimming with varying group sizes (2, 5, 10 fish), along with manually annotated frames for tracking and evaluation. It supports training and benchmarking of deep learning–based multi-object tracking and classification.
The dataset includes raw video data, annotations, model weights, and example predictions. The data processing is described in detail in: Neidhardt, Maximilian, et al. "Robust Motion Tracking and Classification of Zebrafish with Deep Learning." IEEE Access (2026).
The dataset includes raw video data, annotations, model weights, and example predictions. The data processing is described in detail in: Neidhardt, Maximilian, et al. "Robust Motion Tracking and Classification of Zebrafish with Deep Learning." IEEE Access (2026).
Subjects
Deep learning
Fish tracking
Fish trajectory dataset
Locomotion pattern
Physical activity
DDC Class
610: Medicine, Health
006.3: Artificial Intelligence
More Funding Information
This work was partially funded by the TUHH-i3 initiative and the Interdisciplinary Competence Center for Interface Research (ICCIR) supported by Hamburg University of Technology (TUHH) and University Medical Center Hamburg-Eppendorf (UKE), and the RTG 3144 'Multiscale Imaging and Analytics of Interfaces in Musculoskeletal Health' funded by the DFG (Grant 547468385). It received start up funding from the FMTHH (Grant 03fmthh2018).
No Thumbnail Available
Name
FishTracking_Data.zip
Type
Zip
Size
67.89 GB
Format
ZIP
No Thumbnail Available
Name
ReadMe.md
Size
3.78 KB
Format
Markdown