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

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In 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.