Multiple Hypothesis Hypergraph Tracking for Posture Identification in Embryonic Caenorhabditis elegans
This work addresses tracking challenges in biological imaging, specifically for embryonic C. elegans, but appears incremental as it builds on existing MHT methods.
The paper tackled the problem of multiple object tracking under adversarial conditions like volatile motion and noisy detections by developing Multiple Hypothesis Hypergraph Tracking (MHHT), which extended traditional MHT with hypergraphs to model correlated motion, and applied it to track seam cells in embryonic C. elegans, achieving robust tracking in challenging scenarios.
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.