  Collecting overhead imagery using an event camera is desirable due to the
energy efficiency of the image sensor compared to standard cameras. However,
event cameras complicate downstream image processing, especially for complex
tasks such as object detection. In this paper, we investigate the viability of
event streams for overhead object detection. We demonstrate that across a
number of standard modeling approaches, there is a significant gap in
performance between dense event representations and corresponding RGB frames.
We establish that this gap is, in part, due to a lack of overlap between the
event representations and the pre-training data used to initialize the weights
of the object detectors. Then, we apply event-to-video conversion models that
convert event streams into gray-scale video to close this gap. We demonstrate
that this approach results in a large performance increase, outperforming even
event-specific object detection techniques on our overhead target task. These
results suggest that better alignment between event representations and
existing large pre-trained models may result in greater short-term performance
gains compared to end-to-end event-specific architectural improvements.
