鉴定(生物学)
斑马鱼
跟踪(教育)
计算机科学
软件
人工智能
磁道(磁盘驱动器)
卷积神经网络
生物
计算机视觉
计算生物学
心理学
遗传学
基因
操作系统
教育学
植物
作者
Francisco Romero-Ferrero,Mattia G. Bergomi,Robert C. Hinz,Francisco J. H. Heras,Gonzalo G. de Polavieja
出处
期刊:Nature Methods
[Nature Portfolio]
日期:2019-01-08
卷期号:16 (2): 179-182
被引量:295
标识
DOI:10.1038/s41592-018-0295-5
摘要
Understanding of animal collectives is limited by the ability to track each individual. We describe an algorithm and software that extract all trajectories from video, with high identification accuracy for collectives of up to 100 individuals. idtracker.ai uses two convolutional networks: one that detects when animals touch or cross and another for animal identification. The tool is trained with a protocol that adapts to video conditions and tracking difficulty. The idtracker.ai software tracks freely moving animals in large groups of up to 100 individuals. The tool is versatile and has been applied to groups of fruit flies, zebrafish, medaka, ants and mice.
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