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Better Models For People Tracking Matthias Luber Gian Diego Tipaldi Kai O. Arras
 

Summary: Better Models For People Tracking
Matthias Luber Gian Diego Tipaldi Kai O. Arras
Abstract-- People tracking is a key component for
robots operating in populated environments. Previ-
ous works have employed different filtering and data
association techniques for this purpose that typically
rely on a set of generic assumptions on target be-
havior and detector characteristics. In this paper, we
focus on these assumptions rather than the tracking
approach itself and show that with informed models,
people tracking can be made substantially more ac-
curate without compromising efficiency. Concretely,
we present better, human-specific models for the
occurrence of new tracks, false alarms, track occlu-
sions, and track deletions. In the experiments with
a large-scale outdoor data set collected with a laser
range finder, the models and combinations thereof
are experimentally compared using a multi-hypothesis
baseline tracker and the CLEAR MOT metrics. The
results show how some models selectively improve

  

Source: Arras, Kai O. - Institut für Informatik, Albert-Ludwigs-Universität Freiburg

 

Collections: Computer Technologies and Information Sciences