Video Individual Counting and Tracking from Moving Drones: A Benchmark and Methods

Abstract

Counting and tracking dense crowds in large-scale scenes is valuable yet challenging, while existing methods and datasets are largely limited to fixed cameras with small scene coverage. We introduce MovingDroneCrowd++, a large-scale video-level dataset dedicated to dense crowd counting and tracking from moving drones, captured under diverse flight altitudes, camera angles, and illumination conditions. Existing methods, however, still fail to achieve satisfactory Video Individual Counting (VIC) or Multi-Object Tracking (MOT) performance under these challenging aerial conditions. To this end, we propose GD3A (Global Density Map Decomposition via Group-wise Density Assignment) for VIC and GIA-Track (Group-wise Identity Association Tracker) for MOT. Both methods are unified under a framework that aggregates cross-frame pixel-level matches among multiple descriptors per pedestrian through group-wise density assignment (GDA) and group-wise identity association (GIA), respectively. This group-wise aggregation tolerates intra-group spatial mismatches and limits the propagation of inter-group errors. To establish reliable pixel-level descriptor correspondences across frames, we design a frame-pair-conditioned dustbin score inferred from the pedestrian descriptors of each frame pair, enabling Optimal Transport to better distinguish identity differences. Based on these matches, GD3A decomposes global density maps into shared, inflow, and outflow components, while GIA-Track establishes robust pedestrian trajectories. Experiments show that our methods achieve substantial gains in both VIC and MOT on moving-drone videos with dense crowds and complex motions, reducing counting error by 47.4% and improving tracking accuracy by 64.6%. Code, dataset, and pretrained models are available at https://github.com/fyw1999/MovingDroneCrowd.