tracking all individuals in small or large collectives of unmarked animals

Nat Methods. 2019 Feb;16(2):179-182. doi: 10.1038/s41592-018-0295-5. Epub 2019 Jan 14.


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. 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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Animals
  • Behavior, Animal*
  • Computer Graphics
  • Computer Systems
  • Drosophila
  • Image Processing, Computer-Assisted / methods*
  • Neurons / physiology
  • Probability
  • Programming Languages
  • Reference Values
  • Regression Analysis
  • Software*
  • User-Computer Interface
  • Video Recording / methods*
  • Zebrafish