{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/3d-zef-a-3d-zebrafish-tracking-benchmark-1","title":"3D-ZeF: A 3D Zebrafish Tracking Benchmark Dataset","arxiv_id":"2006.08466","date":"2020-06-15","proceeding":"CVPR 2020 6","authors":["Malte Pedersen","Joakim Bruslund Haurum","Stefan Hein Bengtson","Thomas B. Moeslund"],"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 https://vap.aau.dk/3d-zef","url_abs":"https://arxiv.org/abs/2006.08466v1","url_pdf":"https://arxiv.org/pdf/2006.08466v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"3d-zef-a-3d-zebrafish-tracking-benchmark-1","repo_url":"https://github.com/mapeAAU/3D-ZeF","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"3d-multi-object-tracking","task_name":"3D Multi-Object Tracking"},{"task_slug":"3d-object-detection-from-stereo-images","task_name":"3D Object Detection From Stereo Images"},{"task_slug":"multi-object-tracking","task_name":"Multi-Object Tracking"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"faster-r-cnn","method_name":"Faster R-CNN"},{"method_slug":"rpn","method_name":"RPN"},{"method_slug":"roipool","method_name":"RoIPool"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[{"slug":"3d-zef","name":"3D-ZeF","full_name":"3D ZebraFish Tracking Benchmark"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2006.08466","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}