Supporting data for "A dataset of ant colonies motion trajectories in indoor and outdoor scenes to study clustering behavior"

Motion and interaction of social insects (such as ants) have been studied by many researchers to understand the clustering mechanism. Most studies in the field of ant behavior have only focused on indoor environments (laboratory setup), while outdoor environments (natural environments) are still underexplored. In this paper, we collect 10 videos and 3 species of ant colonies from different scenes, including 5 indoor and 5 outdoor scenes. And we develop an image sequence marking software named VisualMarkData, which enables us to provide annotations of ants in the video: (1) offers a comprehensive annotation of states at individual-target as well as colony-target levels; (2) provides a simple matrix format to represent multiple targets and multiple groups of annotations (along with their IDs and behavior labels); (3) during the annotation process, we propose a simple and effective visualization that takes the annotation information of the previous frame as a reference and then simply clicks on the center point of each target to complete the annotation; and (4) we develop a user-friendly windows-based GUI to minimize labor and maximize annotation quality. In all 5,354 frames, the location information and the identification number of each ant are recorded for a total of 712 ants and 114,112 annotations. Moreover, we provide visual analysis tools to assess and validate the technical quality and reproducibility of our data. It is hoped that this dataset will contribute to a deeper exploration of the behavior of the ant colony.

诸多研究者已针对社会性昆虫（如蚂蚁）的运动与交互行为展开研究，以期深入理解其集群行为机制。当前蚁行为领域的多数研究仅聚焦于室内环境（实验室搭建场景），而户外自然环境下的相关探索仍有待深化。本研究收集了来自不同场景的10段视频与3种蚁群样本，其中室内与室外场景各5组。我们开发了一款名为VisualMarkData的图像序列标注软件，可实现对视频中蚂蚁的多维度标注：（1）可针对单一个体与蚁群两个层级提供全面的状态标注；（2）采用简洁的矩阵格式来表示多目标与多组标注信息（包含对应ID与行为标签）；（3）在标注流程中，我们设计了一种简便高效的可视化辅助方案：以上一帧的标注信息作为参照，仅需点击每个目标的中心点即可完成标注；（4）开发了操作友好的Windows端图形用户界面，以降低标注工作量并最大化标注质量。本数据集共计5354帧图像，记录了712只蚂蚁的位置信息与个体编号，总标注量达114112条。此外，我们还提供了可视化分析工具，用于评估与验证本数据集的技术质量与可复现性。期望本数据集能够为蚁群行为的深入研究提供有力支撑。