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In this work we present a novel publicly available stereo based 3D RGBdataset for multi-object zebrafish tracking, called 3D-ZeF. Zebrafish is anincreasingly popular model organism used for studying neurological disorders,drug addiction, and more. Behavioral analysis is often a critical part of suchresearch. However, visual similarity, occlusion, and erratic movement of thezebrafish makes robust 3D tracking a challenging and unsolved problem. Theproposed dataset consists of eight sequences with a duration between 15-120seconds and 1-10 free moving zebrafish. The videos have been annotated with atotal of 86,400 points and bounding boxes. Furthermore, we present a complexityscore and a novel open-source modular baseline system for 3D tracking ofzebrafish. The performance of the system is measured with respect to twodetectors: a naive approach and a Faster R-CNN based fish head detector. Thesystem reaches a MOTA of up to 77.6%. Links to the code and dataset isavailable at the project page https://vap.aau.dk/3d-zef

3D-ZeF: A 3D Zebrafish Tracking Benchmark Dataset