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.

翻译：我们研究了利用基础设施摄像头传感成像的目标足迹不确定性估计问题。在地面坐标与摄像头误差来源之间建立了闭式关系。通过误差传播方程，给定地面点的协方差可表示为摄像头误差的函数。边界框足迹的不确定性随后可表述为目标所有极值点函数的综合结果。为计算地面点的不确定性，需要获知误差来源的典型误差量级。我们提出一种利用静态高精度激光雷达作为真值，通过实验估算典型误差量级的方法。最后，通过CARLA仿真环境中基础设施摄像头的不确定性量化案例研究，展示了不确定性在左转机动过程中的变化规律。