epiTracker: A Framework for Highly Reliable Particle Tracking for the Quantitative Analysis of Fish Movements in Tanks

Abstract

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.