We examine the problem of estimating footprint uncertainty of objects imaged
using the infrastructure based camera sensing. A closed form relationship is
established between the ground coordinates and the sources of the camera
errors. Using the error propagation equation, the covariance of a given ground
coordinate can be measured as a function of the camera errors. The uncertainty
of the footprint of the bounding box can then be given as the function of all
the extreme points of the object footprint. In order to calculate the
uncertainty of a ground point, the typical error sizes of the error sources are
required. We present a method of estimating the typical error sizes from an
experiment using a static, high-precision LiDAR as the ground truth. Finally,
we present a simulated case study of uncertainty quantification from
infrastructure based camera in CARLA to provide a sense of how the uncertainty
changes across a left turn maneuver.Comment: Submitted to IEEE Sensors journa