  We introduce a novel learning-based method for view birdification, the task
of recovering ground-plane trajectories of pedestrians of a crowd and their
observer in the same crowd just from the observed ego-centric video. View
birdification becomes essential for mobile robot navigation and localization in
dense crowds where the static background is hard to see and reliably track. It
is challenging mainly for two reasons; i) absolute trajectories of pedestrians
are entangled with the movement of the observer which needs to be decoupled
from their observed relative movements in the ego-centric video, and ii) a
crowd motion model describing the pedestrian movement interactions is specific
to the scene yet unknown a priori. For this, we introduce a Transformer-based
network referred to as ViewBirdiformer which implicitly models the crowd motion
through self-attention and decomposes relative 2D movement observations onto
the ground-plane trajectories of the crowd and the camera through
cross-attention between views. Most important, ViewBirdiformer achieves view
birdification in a single forward pass which opens the door to accurate
real-time, always-on situational awareness. Extensive experimental results
demonstrate that ViewBirdiformer achieves accuracy similar to or better than
state-of-the-art with three orders of magnitude reduction in execution time.
