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Summary: Tracking Groups of People with a Multi-Model Hypothesis Tracker
Boris Lau Kai O. Arras Wolfram Burgard
Abstract-- People in densely populated environments typi-
cally form groups that split and merge. In this paper we track
groups of people so as to reflect this formation process and gain
efficiency in situations where maintaining the state of individual
people would be intractable. We pose the group tracking
problem as a recursive multi-hypothesis model selection prob-
lem in which we hypothesize over both, the partitioning of
tracks into groups (models) and the association of observations
to tracks (assignments). Model hypotheses that include split,
merge, and continuation events are first generated in a data-
driven manner and then validated by means of the assignment
probabilities conditioned on the respective model. Observations
are found by clustering points from a laser range finder given a
background model and associated to existing group tracks using
the minimum average Hausdorff distance. Experiments with
a stationary and a moving platform show that, in populated
environments, tracking groups is clearly more efficient than
tracking people separately. Our system runs in real-time on a
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