UAV-Diverse: A Benchmark for Multi-Person Tracking in Diverse UAV Scenarios

Robust multi-person tracking across diverse UAV scenarios is critical for real-world UAV applications. Existing UAV multi-object tracking (MOT) benchmarks are typically developed for specific application scenarios, providing limited insight into the cross-scene transfer capability of current trackers. To address this gap, we present UAV-Diverse, a benchmark for evaluating multi-person tracking across diverse UAV scenes. UAV-Diverse evaluates trackers without additional dataset-specific training, providing a unified protocol for assessing cross-scene transfer across three major scenario groups, including competitive sports, wilderness search and patrol, and urban and indoor public spaces, with nine fine-grained subscenes in total. The benchmark exposes key challenges including identity recovery after target disappearance, drastic viewpoint changes, dynamic camera motion, large target scale variations, and severe appearance ambiguity. Extensive evaluations demonstrate that representative MOT methods suffer substantial performance degradation, highlighting the significant challenges that remain in robust multi-person tracking in diverse UAV scenarios. We further provide a simple training-free baseline to facilitate future research.