  We study pursuit-evasion games in highly occluded urban environments, e.g.
tall buildings in a city, where a scout (quadrotor) tracks multiple dynamic
targets on the ground. We show that we can build a neural radiance field (NeRF)
representation of the city -- online -- using RGB and depth images from
different vantage points. This representation is used to calculate the
information gain to both explore unknown parts of the city and track the
targets -- thereby giving a completely first-principles approach to actively
tracking dynamic targets. We demonstrate, using a custom-built simulator using
Open Street Maps data of Philadelphia and New York City, that we can explore
and locate 20 stationary targets within 300 steps. This is slower than a greedy
baseline which which does not use active perception. But for dynamic targets
that actively hide behind occlusions, we show that our approach maintains, at
worst, a tracking error of 200m; the greedy baseline can have a tracking error
as large as 600m. We observe a number of interesting properties in the scout's
policies, e.g., it switches its attention to track a different target
periodically, as the quality of the NeRF representation improves over time, the
scout also becomes better in terms of target tracking.
