  Drone-based crowd tracking faces difficulties in accurately identifying and
monitoring objects from an aerial perspective, largely due to their small size
and close proximity to each other, which complicates both localization and
tracking. To address these challenges, we present the Density-aware Tracking
(DenseTrack) framework. DenseTrack capitalizes on crowd counting to precisely
determine object locations, blending visual and motion cues to improve the
tracking of small-scale objects. It specifically addresses the problem of
cross-frame motion to enhance tracking accuracy and dependability. DenseTrack
employs crowd density estimates as anchors for exact object localization within
video frames. These estimates are merged with motion and position information
from the tracking network, with motion offsets serving as key tracking cues.
Moreover, DenseTrack enhances the ability to distinguish small-scale objects
using insights from the visual-language model, integrating appearance with
motion cues. The framework utilizes the Hungarian algorithm to ensure the
accurate matching of individuals across frames. Demonstrated on DroneCrowd
dataset, our approach exhibits superior performance, confirming its
effectiveness in scenarios captured by drones.
