Automatic detection of fish and tracking of movement for
ecology
Sebastian Lopez-Marcano1,2*, Eric
Jinks1, Christina A. Buelow1,
Christopher J. Brown1, Dadong Wang2,
Branislav Kusy3, Ellen Ditria1, Rod
M. Connolly1
1Coastal and Marine Research Centre, Australian Rivers
Institute, School of Environment and Science, Griffith University, Gold
Coast, QLD 4222, Australia
2Quantitative Imaging Research Team, Data61, CSIRO,
Marsfield, NSW 2122, Australia
3 Data61, CSIRO, QLD 4069, Australia
*corresponding author:
sebastian.lopez-marcano@griffithuni.edu.au
Abstract
- 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.
- We successfully detected yellowfin bream in a multi-species assemblage
(F1 score = 91%). At least 120 of the 169 individual bream present in
videos were correctly identified and tracked. The accuracies
among the three tracking architectures varied, with MOSSE and SiamMask
achieving an accuracy of 78%, and Seq-NMS 84%.
- By employing these emerging computer vision technologies, we
demonstrated a non-invasive and reliable approach to studying fish
behaviour by tracking their movement under field conditions. These
cost-effective technologies potentially will allow future studies to
scale-up analysis of movement across many underwater visual monitoring
systems.