HDST-GNN: Heterogeneous Dynamic Spatiotemporal Graph Neural Networks for Multi-Object Tracking in UAV Aerial Imagery
arXiv:2606.05587v1 Announce Type: cross Abstract: 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 c
The increasing availability of high-resolution UAV imagery and the demand for robust autonomous systems necessitate advanced multi-object tracking solutions. Improvements in graph neural networks and computational power enable these complex models.
Improved multi-object tracking in UAV imagery has direct applications in real-time surveillance, autonomous drone operation, and defense, enhancing situational awareness and operational capabilities.
This research introduces a more sophisticated and adaptive GNN architecture for MOT, addressing specific challenges like varying altitude and object occlusion, thereby improving tracking accuracy and robustness in aerial reconnaissance.
- · Defense contractors
- · UAV manufacturers
- · AI/ML research labs
- · Aerospace & defense sector
- · Security solutions relying on less autonomous or less accurate tracking
- · Human-intensive surveillance operations
Enhanced autonomous capabilities for UAVs in complex environments will become more widespread.
The cost-effectiveness of aerial surveillance and reconnaissance will improve, leading to its broader adoption in both military and civilian sectors.
Increased societal debates around privacy and ethical implications of widespread, highly accurate autonomous aerial tracking.
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Read at arXiv cs.LG