  Tracking individuals is a vital part of many experiments conducted to
understand collective behaviour. Ants are the paradigmatic model system for
such experiments but their lack of individually distinguishing visual features
and their high colony densities make it extremely difficult to perform reliable
tracking automatically. Additionally, the wide diversity of their species'
appearances makes a generalized approach even harder. In this paper, we propose
a data-driven multi-object tracker that, for the first time, employs domain
adaptation to achieve the required generalisation. This approach is built upon
a joint-detection-and-tracking framework that is extended by a set of domain
discriminator modules integrating an adversarial training strategy in addition
to the tracking loss. In addition to this novel domain-adaptive tracking
framework, we present a new dataset and a benchmark for the ant tracking
problem. The dataset contains 57 video sequences with full trajectory
annotation, including 30k frames captured from two different ant species moving
on different background patterns. It comprises 33 and 24 sequences for source
and target domains, respectively. We compare our proposed framework against
other domain-adaptive and non-domain-adaptive multi-object tracking baselines
using this dataset and show that incorporating domain adaptation at multiple
levels of the tracking pipeline yields significant improvements. The code and
the dataset are available at https://github.com/chamathabeysinghe/da-tracker.
