High-resolution, non-invasive animal tracking and reconstruction of local environment in aquatic ecosystems.

ABSTRACT:

Background

Acquiring high resolution quantitative behavioural data underwater often involves installation of costly infrastructure, or capture and manipulation of animals. Aquatic movement ecology can therefore be limited in taxonomic range and ecological coverage.

Methods

Here we present a novel deep-learning based, multi-individual tracking approach, which incorporates Structure-from-Motion in order to determine the 3D location, body position and the visual environment of every recorded individual. The application is based on low-cost cameras and does not require the animals to be confined, manipulated, or handled in any way.

Results

Using this approach, single individuals, small heterospecific groups and schools of fish were tracked in freshwater and marine environ

SUBMITTER: Francisco FA 

PROVIDER: S-EPMC7310323 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature