Heteroskedastic Geospatial Tracking with Distributed Camera Networks

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.

视觉目标跟踪近年来取得了重大进展。然而，这项工作的绝大多数都集中在单个相机的图像平面内跟踪目标，而忽略了与预测目标位置相关的不确定性。在这项工作中，我们专注于利用分布式相机网络的数据解决地理空间目标跟踪问题。目标是在地理空间坐标中预测目标的轨迹以及目标位置的不确定性，同时考虑到禁止集中原始图像数据的通信限制。我们提出了一个新的单目标地理空间跟踪数据集，其中包括高精度的地面真实目标位置以及来自四个相机网络的视频数据。我们提出了一个用于解决此任务的建模框架，包括一个新的骨干模型，并探讨了通过可微跟踪器进行的不确定性校准和微调如何影响性能。