  Thanks to the availability and increasing popularity of Egocentric cameras
such as GoPro cameras, glasses, and etc. we have been provided with a plethora
of videos captured from the first person perspective. Surveillance cameras and
Unmanned Aerial Vehicles(also known as drones) also offer tremendous amount of
videos, mostly with top-down or oblique view-point. Egocentric vision and
top-view surveillance videos have been studied extensively in the past in the
computer vision community. However, the relationship between the two has yet to
be explored thoroughly. In this effort, we attempt to explore this relationship
by approaching two questions. First, having a set of egocentric videos and a
top-view video, can we verify if the top-view video contains all, or some of
the egocentric viewers present in the egocentric set? And second, can we
identify the egocentric viewers in the content of the top-view video? In other
words, can we find the cameramen in the surveillance videos? These problems can
become more challenging when the videos are not time-synchronous. Thus we
formalize the problem in a way which handles and also estimates the unknown
relative time-delays between the egocentric videos and the top-view video. We
formulate the problem as a spectral graph matching instance, and jointly seek
the optimal assignments and relative time-delays of the videos. As a result, we
spatiotemporally localize the egocentric observers in the top-view video. We
model each view (egocentric or top) using a graph, and compute the assignment
and time-delays in an iterative-alternative fashion.
