论文标题
3D-ZEF：3D斑马鱼跟踪基准数据集
3D-ZeF: A 3D Zebrafish Tracking Benchmark Dataset
论文作者
论文摘要
在这项工作中，我们介绍了一种新型的基于立体声的基于立体声的3D RGB数据集，用于多对象斑马鱼跟踪，称为3D-ZEF。斑马鱼是一种日益流行的模型生物，用于研究神经系统疾病，药物成瘾等。行为分析通常是此类研究的关键部分。但是，斑马鱼的视觉相似性，遮挡和不稳定的运动使强大的3D跟踪一个具有挑战性且未解决的问题。所提出的数据集由八个序列组成，持续时间在15-120秒至1-10次自由移动斑马鱼。这些视频已注释，总共有86,400点和边界框。此外，我们提出了一个复杂的得分和用于斑马鱼3D跟踪的新型开源模块基线系统。根据两个检测器来衡量系统的性能：一种天真的方法和更快的R-CNN鱼头检测器。该系统的MOTA高达77.6％。链接到代码和数据集的链接可在项目页面https://vap.aau.dk/3d-zef上找到
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
