  Video surveillance can be significantly enhanced by using both top-view data,
e.g., those from drone-mounted cameras in the air, and horizontal-view data,
e.g., those from wearable cameras on the ground. Collaborative analysis of
different-view data can facilitate various kinds of applications, such as human
tracking, person identification, and human activity recognition. However, for
such collaborative analysis, the first step is to associate people, referred to
as subjects in this paper, across these two views. This is a very challenging
problem due to large human-appearance difference between top and horizontal
views. In this paper, we present a new approach to address this problem by
exploring and matching the subjects' spatial distributions between the two
views. More specifically, on the top-view image, we model and match subjects'
relative positions to the horizontal-view camera in both views and define a
matching cost to decide the actual location of horizontal-view camera and its
view angle in the top-view image. We collect a new dataset consisting of
top-view and horizontal-view image pairs for performance evaluation and the
experimental results show the effectiveness of the proposed method.
