END-TO-END DIFFERENTIABLE NURBS – GEOM-GNN – TGN PIPELINE FOR SPATIOTEMPORAL IDENTIFICATION OF MOVING OBJECTS
Andrii Blyndaruk, Olena Shapovalova · Scientific and Practical Journal "Materials of Scientific Conferences of the Petro Mohyla Black Sea National University" · 2025
A comprehensive approach to moving-object identification is proposed, combining parametric smooth contour representation with rational NURBS curves (Non-Uniform Rational B-Splines), geometry-aware graph processing in Geom-GNN, and event-based temporal memory modeling in TGN. Continuous curve parameters are transformed into a compact spatial graph whose vertices correspond to control points, while edges capture segment connectivity and k-nearest relations. Geom-GNN aggregates messages with distance weighting, and a Temporal Graph Network (TGN) maintains recurrent memory, enabling robust identification as an object traverses its trajectory.