A dataset of ant colonies' motion trajectories in indoor and outdoor scenes to study clustering behavior. (28th October 2022)

• Record Type: Journal Article
• Authors: Wu, Meihong; Cao, Xiaoyan; Yang, Ming; Cao, Xiaoyu; Guo, Shihui
• Is Part Of: GigaScience. Volume 11(2022)
• Publication Date: 2022-10-28
• Subjects: social insects -- outdoor scenes -- image sequence annotation software -- computer vision -- multiobject tracking; Information storage and retrieval systems -- Research -- Periodicals; Biology -- Research -- Periodicals; Medical sciences -- Research -- Periodicals; Database management -- Periodicals; 570.285
• DOI: 10.1093/gigascience/giac096
• Languages: English
• ISSNs: 2047-217X

Abstract

Abstract: Background: The motion and interaction of social insects (such as ants) have been studied by many researchers to understand clustering mechanisms. Most studies in the field of ant behavior have focused only on indoor environments (a laboratory setup), while outdoor environments (natural environments) are still underexplored. Findings: In this article, we collect 10 videos of 3 species of ant colonies from different scenes, including 5 indoor and 5 outdoor scenes. We develop an image sequence marking software named VisualMarkData, which enables us to provide annotations of the ants in the videos. (i) It offers comprehensive annotations of states at the individual-target and colony-target levels. (ii) It provides a simple matrix format to represent multiple targets and multiple groups of annotations (along with their IDs and behavior labels). (iii) 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 a user can simply click on the center point of each target to complete the annotation task. (iv) We develop a user-friendly window-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. Conclusions: We provide a large-scale ant dataset with the accompanying annotation software. It is hoped that our work will contribute to a deeper exploration of the behavior of ant colonies.