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Place-Dependent People Tracking Matthias Luber, Gian Diego Tipaldi and Kai O. Arras

Summary: Place-Dependent People Tracking
Matthias Luber, Gian Diego Tipaldi and Kai O. Arras
Abstract People typically move and act under the constraints of an envi-
ronment, making human behavior strongly place-dependent. Motion patterns,
the places and the rates at which people appear, disappear, walk or stand are
not random but engendered by the environment. In this paper, we learn a
non-homogeneous spatial Poisson process to spatially ground human activity
events for the purpose of people tracking. We show how this representation
can be used to compute refined probability distributions over hypotheses in
a multi-hypothesis tracker and to make better, place-dependent predictions
of human motion. In experiments with data from a laser range finder, we
demonstrate how both extensions lead to more accurate tracking behavior in
terms of data association errors and number of track losses. The system runs
in real-time on a typical desktop computer.
1 Introduction
As robots enter more domains in which they interact and cooperate closely
with humans, people tracking is becoming a key technology for areas such as
human-robot interaction, human activity understanding or intelligent cars. In
contrast to air- and waterborne targets, people typically move and act under
environmental constraints. These constraints vary over space and enable and


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


Collections: Computer Technologies and Information Sciences