  Human identification remains to be one of the challenging tasks in computer
vision community due to drastic changes in visual features across different
viewpoints, lighting conditions, occlusion, etc. Most of the literature has
been focused on exploring human re-identification across viewpoints that are
not too drastically different in nature. Cameras usually capture oblique or
side views of humans, leaving room for a lot of geometric and visual reasoning.
Given the recent popularity of egocentric and top-view vision,
re-identification across these two drastically different views can now be
explored. Having an egocentric and a top view video, our goal is to identify
the cameraman in the content of the top-view video, and also re-identify the
people visible in the egocentric video, by matching them to the identities
present in the top-view video. We propose a CRF-based method to address the two
problems. Our experimental results demonstrates the efficiency of the proposed
approach over a variety of video recorded from two views.
