Multiple Hypothesis Hypergraph Tracking for Posture Identification in Embryonic Caenorhabditis elegans

TL;DR

This paper introduces Multiple Hypothesis Hypergraph Tracking (MHHT), a novel method extending traditional multiple hypothesis tracking with hypergraphs to improve posture identification of embryonic C. elegans amid noisy detections and complex motion.

Contribution

The paper presents MHHT, a new hypergraph-based extension of MHT, specifically designed for robust posture tracking in challenging embryonic C. elegans scenarios.

Findings

MHHT outperforms traditional methods in noisy, volatile conditions.

MHHT accurately tracks seam cell posture during late-stage embryogenesis.

The approach effectively models correlated object motion using hypergraphs.

Abstract

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.