DenseTrack: Drone-based Crowd Tracking via Density-aware Motion-appearance Synergy

TL;DR

DenseTrack is a drone-based crowd tracking framework that leverages crowd density, motion, and appearance cues to improve the accuracy and reliability of tracking small, closely packed objects from aerial videos.

Contribution

It introduces a density-aware tracking method that combines crowd counting, motion, and appearance features, enhancing small object tracking from drone footage.

Findings

Outperforms existing methods on DroneCrowd dataset

Effectively tracks small, densely packed objects from aerial views

Utilizes crowd density as an anchor for precise localization

Abstract

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.