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.

移动动物的行为分析依赖于忠实的记录和轨迹分析来提取相关的运动参数。为了研究群体行为和社会互动，通常需要同时分析个体。为了检测社交互动，例如识别一个群体的领导者而不是追随者，人们需要在整个时间段内对单个轨迹进行无误的分割。虽然存在快速且易于使用的自动跟踪算法，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，我们引入了一种称为epiTracker 的强大算法，用于对二维 (2D) 视频中的多只动物进行分割和跟踪，以及一种易于使用的校正方法，可以获得无错误的分割。我们已经实现了两个图形用户界面，以允许用户友好地控制功能。使用六个标记的 2D 数据集，量化获得准确标签的工作，并与其他可用的软件解决方案进行比较。标记的数据集和软件都是公开可用的。