  Current methods in multiple object tracking (MOT) rely on independent object
trajectories undergoing predictable motion to effectively track large numbers
of objects. Adversarial conditions such as volatile object motion and imperfect
detections create a challenging tracking landscape in which established methods
may yield inadequate results. Multiple hypothesis hypergraph tracking (MHHT) is
developed to perform MOT among interdependent objects amid noisy detections.
The method extends traditional multiple hypothesis tracking (MHT) via
hypergraphs to model correlated object motion, allowing for robust tracking in
challenging scenarios. MHHT is applied to perform seam cell tracking during
late-stage embryogenesis in embryonic C. elegans.
