  The honeybee is a fascinating model animal to investigate how collective
behavior emerges from (inter-)actions of thousands of individuals. Bees may
acquire unique memories throughout their lives. These experiences affect social
interactions even over large time frames. Tracking and identifying all bees in
the colony over their lifetimes therefore may likely shed light on the
interplay of individual differences and colony behavior. This paper proposes a
software pipeline based on two deep convolutional neural networks for the
localization and decoding of custom binary markers that honeybees carry from
their first to the last day in their life. We show that this approach
outperforms similar systems proposed in recent literature. By opening this
software for the public, we hope that the resulting datasets will help
advancing the understanding of honeybee collective intelligence.
