idtracker.ai: tracking all individuals in small or large collectives of unmarked animals

By Francisco Romero-Ferrero, M. Bergomi, R. Hinz, Francisco J. H. Heras, G. D. de Polavieja. Nature Methods, 2019

343 citations recorded by the source corpus.

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.

Subfields: Cell Biology, Developmental Biology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/df8b8509e85bfe945ac5dbd77bc88c3150c5da67

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1. Qinfeng Gao
2. Song Zhang
3. Shuanglin Dong
4. Jintao Liu
5. Chao Zhou
6. Xinting Yang

Citations in the corpus

• Deep learning for smart fish farming: applications, opportunities and challenges (2020, methodology): “When using deep learning to classify fish behavior, crossing, overlapping and blocking caused by free-swimming fish (Zhao et al., 2018a ; Romero-Ferrero et al., 2019) and low-quality environmental images (Zhou et al., 2019) form the main challenges to behavior analysis; thus, these problems need to ”