Laboratory of Social Evolution and Behavior, The Rockefeller University, New York, United States.
Elife. 2020 Nov 19;9:e58145. doi: 10.7554/eLife.58145.
Recent years have seen a surge in methods to track and analyze animal behavior. Nevertheless, tracking individuals in closely interacting, group-living organisms remains a challenge. Here, we present anTraX, an algorithm and software package for high-throughput video tracking of color-tagged insects. anTraX combines neural network classification of animals with a novel approach for representing tracking data as a graph, enabling individual tracking even in cases where it is difficult to segment animals from one another, or where tags are obscured. The use of color tags, a well-established and robust method for marking individual insects in groups, relaxes requirements for image size and quality, and makes the software broadly applicable. anTraX is readily integrated into existing tools and methods for automated image analysis of behavior to further augment its output. anTraX can handle large-scale experiments with minimal human involvement, allowing researchers to simultaneously monitor many social groups over long time periods.
近年来,用于跟踪和分析动物行为的方法层出不穷。然而,在密切相互作用、群体生活的生物中跟踪个体仍然是一个挑战。在这里,我们提出了 anTraX,这是一个用于标记昆虫的高通量视频跟踪的算法和软件包。anTraX 将动物的神经网络分类与一种新的方法相结合,即将跟踪数据表示为图,即使在难以将动物彼此分割或标签被遮挡的情况下,也能实现个体跟踪。使用颜色标记是一种在群体中标记个体昆虫的成熟且稳健的方法,它放宽了对图像大小和质量的要求,使软件具有广泛的适用性。anTraX 可以很容易地集成到现有的行为自动图像分析工具和方法中,以进一步增强其输出。anTraX 可以在最小的人工干预下处理大规模实验,允许研究人员在长时间内同时监测许多社会群体。