Automated visual tracking of animals is rapidly becoming an indispensable tool for the study of behavior. It offers a quantitative methodology by which organisms' sensing and decision-making can be studied in a wide range of ecological contexts. Despite this, existing solutions tend to be challenging to deploy in practice, especially when considering long and/or high-resolution video-streams. Here, we present TRex, a fast and easy-to-use solution for tracking a large number of individuals simultaneously using background-subtraction with real-time (60Hz) tracking performance for up to approximately 256 individuals and estimates 2D visual-fields, outlines, and head/rear of bilateral animals, both in open and closed-loop contexts. Additionally, TRex offers highly-accurate, deep-learning-based visual identification of up to approximately 100 unmarked individuals, where it is between 2.5-46.7 times faster, and requires 2-10 times less memory, than comparable software (with relative performance increasing for more organisms/longer videos) and provides interactive data-exploration within an intuitive, platform-independent graphical user-interface.

中文翻译：

---

TRex，一种快速的多动物跟踪系统，具有无标记识别以及姿势和视野的2D估计

动物的自动视觉跟踪正迅速成为行为研究中必不可少的工具。它提供了一种定量方法，可以在广泛的生态环境中研究生物的感知和决策。尽管如此，现有解决方案在实践中往往难以部署，特别是在考虑长和/或高分辨率视频流时。在这里，我们介绍TRex，这是一种快速且易于使用的解决方案，用于通过背景扣除与实时（60Hz）跟踪性能同时跟踪大量个体，最多可跟踪约256个个体，并估计2D视野，轮廓，以及开环和闭环环境中的双边动物的头/尾。此外，TRex还提供高精度，