Automated Fish Tracking for Ecology

Published March 2, 2021 | Version fish-track2

Software Open

1. Coastal and Marine Research Centre, Australian Rivers Institute, School of Environment and Science, Griffith University, Gold Coast, QLD 4222, Australia
2. Quantitative Imaging Research Team, Data61, CSIRO, Marsfield, NSW 2122, Australia
3. Data61, CSIRO, QLD 4069, Australia

Animal movement studies are conducted to monitor ecosystem health, understand ecological dynamics and address management and conservation questions. In marine environments, traditional sampling and monitoring methods to measure animal movement are invasive, labour intensive, costly, and measuring movement of many individuals is challenging. Automated detection and tracking of small-scale movements of many animals through cameras are possible. However, automated techniques are largely untested in field conditions, and this is hampering applications to ecological questions.
Here, we aimed to test the ability of computer vision algorithms to track small-scale movement of many individuals in videos. We apply the method to track fish movement in the field and characterize movement behaviour. First, we automated the detection of a common fisheries species (yellowfin bream, Acanthopagrus australis) from underwater videos of individuals swimming along a known movement corridor. We then tracked fish movement with three types of tracking algorithms (MOSSE, Seq-NMS and SiamMask), and evaluated their accuracy at characterizing movement.
This repository includes all the object detection training images and annotations, movement dataset annotations, images and videos and the tracking and data wrangling scripts.

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slopezmarcano/automated-fish-tracking-fish-track2.zip md5:5f6432b3a215d859917d11c6bcce4edd	76.8 MB	Preview Download

Is supplement to: https://github.com/slopezmarcano/automated-fish-tracking/tree/fish-track2 (URL)