| Abstract | Visibility in architectural layouts affects humannavigation, so a suitable representation of visibility context is
useful in understanding human activity. Motivated by studies of
spatial behavior, we use a set of features from visibility analysis
to represent spatial context in the interpretation of human
activity. An agent’s goal, belief about the world, trajectory and
visible layout are considered to be random variables that evolve
with time during the agent’s movement, and are modeled in
a Bayesian framework. We design a search-based task in a
sprite-world, and compare the results of our framework to
those of human subject experiments. Our findings confirm that
knowledge of spatial layout improves human interpretations of
the trajectories (implying that visibility context is useful in this
task). Since our framework demonstrates performance close to
that of human subjects with knowledge of spatial layout, our
findings confirm that our model makes adequate use of visibility
context. In addition, the representation we use for visibility
context allows our model to generalize well when presented
with new scenes.
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