Institute of Molecular and Cell Biology, Mannheim University of Applied Sciences, Mannheim, Germany.
Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology, Baden-Württemberg, Germany.
SLAS Technol. 2021 Aug;26(4):367-376. doi: 10.1177/2472630320977454. Epub 2020 Dec 21.
Behavioral analysis of moving animals relies on a faithful recording and track analysis to extract relevant parameters of movement. To study group behavior and social interactions, often simultaneous analyses of individuals are required. To detect social interactions, for example to identify the leader of a group as opposed to followers, one needs an error-free segmentation of individual tracks throughout time. While automated tracking algorithms exist that are quick and easy to use, inevitable errors will occur during tracking. To solve this problem, we introduce a robust algorithm called epiTracker for segmentation and tracking of multiple animals in two-dimensional (2D) videos along with an easy-to-use correction method that allows one to obtain error-free segmentation. We have implemented two graphical user interfaces to allow user-friendly control of the functions. Using six labeled 2D datasets, the effort to obtain accurate labels is quantified and compared to alternative available software solutions. Both the labeled datasets and the software are publicly available.
对运动动物的行为分析依赖于对运动相关参数进行准确的记录和轨迹分析。为了研究群体行为和社会互动，通常需要同时对个体进行分析。为了检测社会互动，例如识别群体中的领导者与跟随者，需要在整个时间过程中对个体轨迹进行无错误的分割。虽然存在快速易用的自动跟踪算法，但在跟踪过程中不可避免地会出现错误。为了解决这个问题，我们引入了一种名为 epiTracker 的强大算法，用于在二维 (2D) 视频中对多个动物进行分割和跟踪，并提供了一种易于使用的校正方法，允许获得无错误的分割。我们已经实现了两个图形用户界面，以方便用户友好地控制功能。使用六个标记的 2D 数据集，量化了获得准确标签的工作量，并与其他可用的软件解决方案进行了比较。标记数据集和软件都是公开可用的。
