Authors: Allison Bruce and Geoffrey Gordon
From: ICRA 2004
Abstract:An important building block for intelligent mobile
robots is the ability to track people moving around in the environment.
Algorithms for person-tracking often incorporate motion
models, which can improve tracking accuracy by predicting how
people will move. More accurate motion models produce better
tracking because they allow us to average together multiple
predictions of the person’s location rather than depending
entirely on the most recent observation. Many implemented
systems, however, use simple conservative motion models such
as Brownian motion (in which the person’s direction of motion
is independent on each time step). We present an improved
motion model based on the intuition that people tend to follow
efficient trajectories through their environments rather than
random paths. Our motion model learns common destinations
within the environment by clustering training examples of actual
trajectories, then uses a path planner to predict how a person
would move along routes from his or her present location
to these destinations. We have integrated this motion model
into a particle-filter-based person-tracker, and we demonstrate
experimentally that our new motion model performs significantly
better than simpler models, especially in situations in which there
are extended periods of occlusion during tracking.
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