Computing Consensus Curves

TL;DR

This paper introduces a novel global method for extracting accurate average ant trajectories from multiple input trajectories, leveraging a new network flow variant and heuristics that outperform existing automated systems.

Contribution

The paper presents a new global approach based on edge-disjoint paths in an ant-interaction graph, with heuristics that effectively solve an NP-hard problem in practice.

Findings

Heuristic algorithms outperform existing automated systems.

The global method improves accuracy of trajectory extraction.

The approach is effective despite the NP-hardness of the underlying problem.

Abstract

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.