Contraining models of collective motion in biological systems
Animals moving together as one is a commonly seen spectacle in both the sky, with flock of birds, and in the oceans, with school of fish.
Mathematical models have been developed over the last 50 years to gain a deeper understanding into how such coordination occurs. There has been extensive numerical simulation and analysis done for these models but little comparison to actual data.
My research will describe a computer vision algorithm we devised to detect and track individual sheep in drone footage we collected. The algorithm emphasises the differences in the colours of the sheep and the grass background. In total the trajectories of 45 or more sheep were extracted from 14 videos. In some of these videos the quadbike and farmers herding the sheep were also able to be tracked. From these trajectories we were able to look at quantities such as average speed and global alignment which can then be used to compare to simulated data.
I will go on to compare our observational data to two different types of models: one to type of model to compare to emergent flocking behaviour and another type of model to compare to my observations of “steady-state” flocking. I will compare our observational data to simulated data using an approximate Bayesian computation rejection scheme to calculate an approximate joint posterior distribution for the parameters in each of the models.
