  We consider the problem of extracting accurate average ant trajectories from
many (possibly inaccurate) input trajectories contributed by citizen
scientists. Although there are many generic software tools for motion tracking
and specific ones for insect tracking, even untrained humans are much better at
this task, provided a robust method to computing the average trajectories. We
implemented and tested several local (one ant at a time) and global (all ants
together) method. Our best performing algorithm uses a novel global method,
based on finding edge-disjoint paths in an ant-interaction graph constructed
from the input trajectories. The underlying optimization problem is a new and
interesting variant of network flow. Even though the problem is NP-hard, we
implemented two heuristics, which work very well in practice, outperforming all
other approaches, including the best automated system.
