Heteroskedastic Geospatial Tracking with Distributed Camera Networks
Colin Samplawski Shiwei Fang Ziqi Wang Deepak Ganesan Mani Srivastava Benjamin M. Marlin
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.