  In this paper, we show that paint markings are a feasible approach to
automatize the analysis of behavioral assays involving honey bees in the field
where marking has to be as lightweight as possible. We contribute a novel
dataset for bees re-identification with paint-markings with 4392 images and 27
identities. Contrastive learning with a ResNet backbone and triplet loss led to
identity representation features with almost perfect recognition in closed
setting where identities are known in advance. Diverse experiments evaluate the
capability to generalize to separate IDs, and show the impact of using
different body parts for identification, such as using the unmarked abdomen
only. In addition, we show the potential to fully automate the visit detection
and provide preliminary results of compute time for future real-time deployment
in the field on an edge device.
