Argos: A toolkit for tracking multiple animals in complex visual environments. Issue 3 (10th December 2021) - Ray, Subhasis; Stopfer, Mark A.
Abstract: Automatically tracking the positions of multiple animals is often necessary for studying behaviours. This task involves multiple object tracking, a challenging problem in computer vision. Recent advances in machine learning applied to video analysis have been helpful for animal tracking. However, existing tools work well only in homogeneous environments with uniform illumination, features rarely found in natural settings. Moreover, available algorithms cannot effectively process discontinuities in animal motion such as sudden jumps, thus requiring laborious manual review. Here we present Argos, a software toolkit for tracking multiple animals in inhomogeneous environments. Argos includes tools for compressing videos based on animal movement, for generating training sets for a convolutional neural network (CNN) to detect animals, for tracking multiple animals in a video and for facilitating review and correction of the tracks manually, with simple graphical user interfaces. We demonstrate that Argos can help reduce the amount of video data to be stored and analysed, speed up analysis and allow analysing difficult and ambiguous conditions in a scene. Thus, Argos supports multiple approaches to animal tracking suited for varying recording conditions and available computational resources. Together, these tools allow the recording and tracking of movements of multiple markerless animals in inhomogeneous environments over many hours.