  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 https://vap.aau.dk/3d-zef
