2023/06/04 by Colin Samplawski, Shiwei Fang, Samplawski, Colin +9 · 1 citation
Computer Science · Engineering · #Advanced Image and Video Retrieval Techniques #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #Distributed #FOS: Computer and information sciences #Machine Learning (cs.LG) #Parallel #Vehicular Ad Hoc Networks (VANETs) #Video Surveillance and Tracking Methods #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2306.02407
openalex publication_date 2023/06/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.