Preliminary Large-scale computer vision-based analysis of ant foraging dynamics enabled by novel mac

在我的博士学位上，我想在这些第一个结果的基础上，详细研究单个蚂蚁任务选择的个体发生的因素，以及这些选择如何导致在群体水平上服务的行为模式，特别是当以不同性质的食物来源为食时，群体如何调整它们觅食群体的大小分布。为此，我将继续开发通用的计算机视觉和机器学习工具。特别是，我将创建允许在非连续记录中重复识别个人的分类器。这种识别背后的深度学习算法所需的训练数据将通过与德国伍珀塔尔的动物园合作提供，该园已经建立。我们将在动物园进行现场实验，并鼓励参观者参与数据分析。通过这种“公民科学”的方法，我们将能够以前所未有的规模训练和测试我们的算法。该项目的结果将为利用现代工程方法对一个基本的生物学问题提供新的见解--适应优势，从而进化真社会昆虫的劳动分工。事实上，在这个项目中开发的模型将是通用的，并适用于分析大量个人之间的各种相互作用。

n my PhD, I want to build on these first results and to study in detail the factors which underlie theontogeny of task choices in individual ants, and how these choices result in behavioural patternsobserved at the level of the colony, in particular how the colony adjusts the size-distribution of theirforaging parties when feeding on food sources of different properties. To this end, I will continue thedevelopment of versatile computer vision and machine learning tools. In particular, I will createclassifiers which allow the repeated identification of individuals across non-continuous recordings. Thetraining data required to power the deep learning algorithms behind this recognition will be providedthrough a cooperation with the Zoological Garden in Wuppertal, Germany, which has already been setup. We will conduct live experiments in the zoo, and encourage visitors to partake in data analysis.Through this "citizen science" approach, we will be able to train and test our algorithms on anunprecedented scale. The results of this project will provide novel insights into a fundamental biologicalquestion - the adaptive advantage and hence evolution of division of labour in eusocial insects - usingmodern engineering methods. Indeed, the models developed in this project will be versatile andapplicable in the analysis of various interactions between a large number of individuals.