  Computational approaches to the analysis of collective behavior in social
insects increasingly rely on motion paths as an intermediate data layer from
which one can infer individual behaviors or social interactions. Honey bees are
a popular model for learning and memory. Previous experience has been shown to
affect and modulate future social interactions. So far, no lifetime history
observations have been reported for all bees of a colony. In a previous work we
introduced a tracking system customized to track up to $4000$ bees over several
weeks. In this contribution we present an in-depth description of the
underlying multi-step algorithm which both produces the motion paths, and also
improves the marker decoding accuracy significantly. We automatically tracked
${\sim}2000$ marked honey bees over 10 weeks with inexpensive recording
hardware using markers without any error correction bits. We found that the
proposed two-step tracking reduced incorrect ID decodings from initially
${\sim}13\%$ to around $2\%$ post-tracking. Alongside this paper, we publish
the first trajectory dataset for all bees in a colony, extracted from ${\sim}
4$ million images. We invite researchers to join the collective scientific
effort to investigate this intriguing animal system. All components of our
system are open-source.
