The paper introduces HDST-GNN, a heterogeneous dynamic spatiotemporal GNN for UAV multi-object tracking with altitude-adaptive edges, typed nodes, and occlusion-gated aggregation, reporting 94.51% MOTA on VisDrone2019-MOT.
GCNNMatch: Graph Convolu- tional Neural Networks for Multi-Object Tracking via Sinkhorn Normalization
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HDST-GNN: Heterogeneous Dynamic Spatiotemporal Graph Neural Networks for Multi-Object Tracking in UAV Aerial Imagery
The paper introduces HDST-GNN, a heterogeneous dynamic spatiotemporal GNN for UAV multi-object tracking with altitude-adaptive edges, typed nodes, and occlusion-gated aggregation, reporting 94.51% MOTA on VisDrone2019-MOT.