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.

翻译：个体追踪是理解群体行为实验中的关键环节。蚂蚁是此类实验的标志性模型系统，但其缺乏个体可区分的视觉特征，且蚁群密度极高，使得自动化的可靠追踪极为困难。此外，蚂蚁物种外观的多样性使得通用化方法更具挑战性。本文首次提出一种数据驱动的多目标跟踪器，通过域适应技术实现所需的泛化能力。该方法基于联合检测与跟踪框架扩展而来，集成了一组域判别模块，在跟踪损失之外引入对抗训练策略。除这一创新性的域自适应跟踪框架外，我们还为蚂蚁追踪问题提供了新数据集与基准测试集。该数据集包含57段完整轨迹标注的视频序列，涵盖来自两种不同蚂蚁物种、在不同背景图案上移动的30,000帧图像。其中源域与目标域分别包含33段与24段序列。我们通过该数据集将所提框架与其他域自适应及非域自适应多目标跟踪基线进行对比，表明在跟踪流程的多个层级引入域适应可带来显著性能提升。代码与数据集已开源发布于 https://github.com/chamathabeysinghe/da-tracker。