  In the past decade many robots were deployed in the wild, and people
detection and tracking is an important component of such deployments. On top of
that, one often needs to run modules which analyze persons and extract higher
level attributes such as age and gender, or dynamic information like gaze and
pose. The latter ones are especially necessary for building a reactive, social
robot-person interaction.
  In this paper, we combine those components in a fully modular
detection-tracking-analysis pipeline, called DetTA. We investigate the benefits
of such an integration on the example of head and skeleton pose, by using the
consistent track ID for a temporal filtering of the analysis modules'
observations, showing a slight improvement in a challenging real-world
scenario. We also study the potential of a so-called "free-flight" mode, where
the analysis of a person attribute only relies on the filter's predictions for
certain frames. Here, our study shows that this boosts the runtime
dramatically, while the prediction quality remains stable. This insight is
especially important for reducing power consumption and sharing precious
(GPU-)memory when running many analysis components on a mobile platform,
especially so in the era of expensive deep learning methods.
