DenseTrack: Drone-based Crowd Tracking via Density-aware Motion-appearance Synergy
This addresses the problem of tracking small, closely spaced objects in aerial drone footage for applications like surveillance or crowd monitoring, but it appears incremental as it builds on existing tracking and crowd counting methods.
The paper tackled drone-based crowd tracking by proposing the DenseTrack framework, which uses density-aware motion-appearance synergy to improve accuracy, achieving superior performance on the DroneCrowd dataset.
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.