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

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, the high occlusion frequency, visual similarity, 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; and a complexity measure, based on the level of occlusions, is provided for all the sequences. Furthermore, we present a novel open-source modular baseline system for 3D tracking of zebrafish. The performance of the system is measured on behalf of two detectors: a naive approach and a Faster R-CNN based fish head detector. The system reaches a MOTA of up to 77.6% but there are indications that the trained head detector do not generalize well between different cohorts of zebrafish.