针对无人机影像多目标跟踪，提出动态异质图网络，显著减少身份切换。
HDST-GNN: Heterogeneous Dynamic Spatiotemporal Graph Neural Networks for Multi-Object Tracking in UAV Aerial Imagery
- 按检测、活跃轨迹、丢失轨迹分三类节点，动态调整连接半径适应高度变化
- 在VisDrone数据集上达94.51% MOTA，身份切换比SORT少81%
- 适合小目标密集、遮挡频繁的无人机监控场景
从无人机影像中进行多目标跟踪（MOT）面临独特挑战：序列间飞行高度变化大，目标尺寸小且密集，频繁遮挡导致身份切换。现有基于图的追踪器假设空间上下文固定，对所有对象一视同仁，忽略了检测、活跃轨迹和丢失目标在生命周期中的异质性。本文提出HDST-GNN，一种异质动态时空图神经网络，包含三项创新：首先，通过平均目标面积估计相机高度并自适应调整图连接半径；其次，将检测（类型D）、确认轨迹（类型T）和丢失轨迹（类型L）作为不同节点类型，使用专用投影与类型化边关系建模；第三，采用遮挡门控时间聚合机制，根据遮挡置信度控制节点注意力贡献，防止被遮挡节点污染邻居特征。HDST-GNN采用可微分的Sinkhorn头，联合交叉熵与三元组损失端到端训练。在使用真值检测的VisDrone2019-MOT上，取得94.51% MOTA和97.24% IDF1，较SORT提升5.0 MOTA点，身份切换减少81%；使用真实YOLOv8n检测时，身份切换减少49%。消融实验验证各模块独立贡献。
原文摘要 · Abstract (English)
Multi-object tracking (MOT) from UAV imagery presents unique challenges: altitude varies across sequences, objects are small and densely packed, and frequent occlusion causes identity switches. Existing graph-based trackers assume fixed spatial context and treat all objects uniformly, ignoring the heterogeneous lifecycle states of detections, active tracklets, and lost targets. We propose HDST-GNN, a Heterogeneous Dynamic Spatiotemporal Graph Neural Network with three novel contributions. First, Altitude-Adaptive Edge Construction estimates a camera-altitude proxy from mean object area and adjusts the graph connectivity radius accordingly. Second, Heterogeneous Node Representation models detections (Type-D), confirmed tracklets (Type-T), and lost tracklets (Type-L) as distinct node types with dedicated projections and typed edge relations. Third, Occlusion-Gated Temporal Aggregation gates each node's attention contribution by its occlusion confidence, preventing occluded nodes from corrupting neighbour embeddings. HDST-GNN is trained end-to-end with a differentiable Sinkhorn head using joint cross-entropy and triplet loss. On VisDrone2019-MOT with oracle detections, HDST-GNN achieves 94.51% MOTA and 97.24% IDF1, outperforming SORT by +5.0 MOTA points and reducing identity switches by 81%. With real YOLOv8n detections, HDST-GNN reduces identity switches by 49% vs. SORT. Ablation studies confirm the independent contribution of each component.
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