Tracking Solar Events through Iterative Refinement

In this paper, we combine two approaches to multiple-target tracking: the first is a hierarchical approach to iteratively growing track fragments across gaps in detections; the second is a network flow based optimization method for data association. The network flow based optimization method is utilized for data association in an iteratively growing manner. This process is applied to solar data, retrieved from the Heliophysics Event Knowledge base (HEK) and utilizes precomputed image parameter values. These precomputed image parameter values are used to compare visual similarity of detected events, to determine the best matching track fragment associations, which leads to a globally optimal track fragment association hypothesis.