Visual object tracking has seen significant progress in recent years.
However, the vast majority of this work focuses on tracking objects within the
image plane of a single camera and ignores the uncertainty associated with
predicted object locations. In this work, we focus on the geospatial object
tracking problem using data from a distributed camera network. The goal is to
predict an object's track in geospatial coordinates along with uncertainty over
the object's location while respecting communication constraints that prohibit
centralizing raw image data. We present a novel single-object geospatial
tracking data set that includes high-accuracy ground truth object locations and
video data from a network of four cameras. We present a modeling framework for
addressing this task including a novel backbone model and explore how
uncertainty calibration and fine-tuning through a differentiable tracker affect
performance.

本研究旨在解决使用分布式摄像头网络进行地理空间物体跟踪的问题，界定物体的地理空间坐标和物体位置的不确定性，提出了一个新颖的单个物体地理空间跟踪数据集，并探讨了不确定性校准和通过可微的跟踪器对模型进行微调对性能的影响。