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.

翻译：视觉目标跟踪近年来取得了显著进展。然而，绝大多数工作聚焦于在单台相机的图像平面内跟踪目标，并忽略了预测目标位置的不确定性。本研究聚焦于利用分布式相机网络数据进行地理空间目标跟踪问题。目标是预测目标在地理空间坐标中的轨迹及其位置不确定性，同时遵守禁止集中处理原始图像数据的通信约束。我们提出了一种新颖的单目标地理空间跟踪数据集，其中包含高精度真实目标位置数据以及来自四台相机网络的视频数据。我们提出了一套解决此任务的建模框架，包含一个新颖的骨干模型，并探讨了不确定性校准以及通过可微跟踪器进行微调对性能的影响。