  In this paper, we present a new approach to bridge the domain gap between
synthetic and real-world data for un- manned aerial vehicle (UAV)-based
perception. Our formu- lation is designed for dynamic scenes, consisting of
moving objects or human actions, where the goal is to recognize the pose or
actions. We propose an extension of K-Planes Neural Radiance Field (NeRF),
wherein our algorithm stores a set of tiered feature vectors. The tiered
feature vectors are generated to effectively model conceptual information about
a scene as well as an image decoder that transforms output feature maps into
RGB images. Our technique leverages the information amongst both static and
dynamic objects within a scene and is able to capture salient scene attributes
of high altitude videos. We evaluate its performance on challenging datasets,
including Okutama Action and UG2, and observe considerable improvement in
accuracy over state of the art aerial perception algorithms.
