3D-ZeF: A 3D Zebrafish Tracking Benchmark Dataset
Abstract
In this work we present a novel publicly available stereo based 3D RGB dataset for multi-object zebrafish tracking, called 3D-ZeF. Zebrafish is an increasingly popular model organism used for studying neurological disorders, drug addiction, and more. Behavioral analysis is often a critical part of such research. However, visual similarity, occlusion, and erratic movement of the zebrafish makes robust 3D tracking a challenging and unsolved problem. The proposed dataset consists of eight sequences with a duration between 15-120 seconds and 1-10 free moving zebrafish. The videos have been annotated with a total of 86,400 points and bounding boxes. Furthermore, we present a complexity score and a novel open-source modular baseline system for 3D tracking of zebrafish. The performance of the system is measured with respect to two detectors: a naive approach and a Faster R-CNN based fish head detector. The system reaches a MOTA of up to 77.6%. Links to the code and dataset is available at the project page http://vap.aau.dk/3d-zef
Cite
Text
Pedersen et al. "3D-ZeF: A 3D Zebrafish Tracking Benchmark Dataset." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020. doi:10.1109/CVPR42600.2020.00250Markdown
[Pedersen et al. "3D-ZeF: A 3D Zebrafish Tracking Benchmark Dataset." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.](https://mlanthology.org/cvpr/2020/pedersen2020cvpr-3dzef/) doi:10.1109/CVPR42600.2020.00250BibTeX
@inproceedings{pedersen2020cvpr-3dzef,
title = {{3D-ZeF: A 3D Zebrafish Tracking Benchmark Dataset}},
author = {Pedersen, Malte and Haurum, Joakim Bruslund and Bengtson, Stefan Hein and Moeslund, Thomas B.},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2020},
doi = {10.1109/CVPR42600.2020.00250},
url = {https://mlanthology.org/cvpr/2020/pedersen2020cvpr-3dzef/}
}