  We introduce view birdification, the problem of recovering ground-plane
movements of people in a crowd from an ego-centric video captured from an
observer (e.g., a person or a vehicle) also moving in the crowd. Recovered
ground-plane movements would provide a sound basis for situational
understanding and benefit downstream applications in computer vision and
robotics. In this paper, we formulate view birdification as a geometric
trajectory reconstruction problem and derive a cascaded optimization method
from a Bayesian perspective. The method first estimates the observer's movement
and then localizes surrounding pedestrians for each frame while taking into
account the local interactions between them. We introduce three datasets by
leveraging synthetic and real trajectories of people in crowds and evaluate the
effectiveness of our method. The results demonstrate the accuracy of our method
and set the ground for further studies of view birdification as an important
but challenging visual understanding problem.
