Abstract Tracking movement of insects in a social group (such as ants) is challenging, because the individuals are not only similar in appearance but also likely to perform intensive body contact and sudden movement adjustment (start/stop, direction changes). To address this challenge, we introduce an online multi-object tracking framework that combines both the motion and appearance information of ants. We obtain the appearance descriptors by using the ResNet model for offline training on a small (N=50) sample dataset. For online association, a cosine similarity metric computes the matching degree between historical appearance sequences of the trajectory and the current detection. We validate our method in both indoor (lab setup) and outdoor video sequences. The results show that our model obtains 99.3% ± 0.5% MOTA and 91.9% ± 2.1% MOTP across 24,050 testing samples in five indoor sequences, with real-time tracking performance. In an outdoor sequence, we achieve 99.3% MOTA and 92.9% MOTP across 22,041 testing samples. The datasets and code are made publicly available for future research in relevant domains.

中文翻译：

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基于深度关联度量的蚂蚁在线跟踪：方法、数据集和评估

摘要 跟踪社会群体中昆虫（如蚂蚁）的运动具有挑战性，因为个体不仅外观相似，而且可能进行密集的身体接触和突然的运动调整（开始/停止、方向改变）。为了应对这一挑战，我们引入了一个在线多目标跟踪框架，该框架结合了蚂蚁的运动和外观信息。我们通过使用 ResNet 模型在小型 (N=50) 样本数据集上进行离线训练来获得外观描述符。对于在线关联，余弦相似度度量计算轨迹的历史出现序列与当前检测之间的匹配度。我们在室内（实验室设置）和室外视频序列中验证了我们的方法。结果表明，我们的模型在 24 年中获得了 99.3% ± 0.5% MOTA 和 91.9% ± 2.1% MOTP，5个室内序列的050个测试样本，具有实时跟踪性能。在室外序列中，我们在 22,041 个测试样本中实现了 99.3% 的 MOTA 和 92.9% 的 MOTP。数据集和代码已公开用于相关领域的未来研究。