  The ability to determine what parts of objects and surfaces people touch as
they go about their daily lives would be useful in understanding how the
COVID-19 virus spreads. To determine whether a person has touched an object or
surface using visual data, images, or videos, is a hard problem. Computer
vision 3D reconstruction approaches project objects and the human body from the
2D image domain to 3D and perform 3D space intersection directly. However, this
solution would not meet the accuracy requirement in applications due to
projection error. Another standard approach is to train a neural network to
infer touch actions from the collected visual data. This strategy would require
significant amounts of training data to generalize over scale and viewpoint
variations. A different approach to this problem is to identify whether a
person has touched a defined object. In this work, we show that the solution to
this problem can be straightforward. Specifically, we show that the contact
between an object and a static surface can be identified by projecting the
object onto the static surface through two different viewpoints and analyzing
their 2D intersection. The object contacts the surface when the projected
points are close to each other; we call this cross view projection consistency.
Instead of doing 3D scene reconstruction or transfer learning from deep
networks, a mapping from the surface in the two camera views to the surface
space is the only requirement. For planar space, this mapping is the Homography
transformation. This simple method can be easily adapted to real-life
applications. In this paper, we apply our method to do office occupancy
detection for studying the COVID-19 transmission pattern from an office desk in
a meeting room using the contact information.
