Video Individual Counting and Tracking from Moving Drones: A Benchmark and Methods
Yaowu Fan, Jia Wan, Tao Han, Andy J. Ma, and Antoni B. Chan

TL;DR
This paper introduces MovingDroneCrowd++, a large drone-captured dataset for dense crowd counting and tracking, and proposes GD3A and DVTrack methods that significantly improve accuracy in complex, large-scale scenes.
Contribution
The paper presents the first large-scale drone-based dataset and novel density map and descriptor-based methods for crowd counting and tracking in dynamic scenes.
Findings
GD3A reduces counting error by 47.4%
DVTrack improves tracking performance by 39.2%
Methods outperform existing approaches in dense, complex scenes
Abstract
Counting and tracking dense crowds in large-scale scenes is highly challenging, yet existing methods mainly rely on datasets captured by fixed cameras, which provide limited spatial coverage and are inadequate for large-scale dense crowd analysis. To address this limitation, we propose a flexible solution using moving drones to capture videos and perform video-level crowd counting and tracking of unique pedestrians across entire scenes. We introduce MovingDroneCrowd++, the largest video-level dataset for dense crowd counting and tracking captured by moving drones, covering diverse and complex conditions with varying flight altitudes, camera angles, and illumination. Existing methods fail to achieve satisfactory performance on this dataset. To this end, we propose GD3A (Global Density Map Decomposition via Descriptor Association), a density map-based video individual counting method that…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsVideo Surveillance and Tracking Methods · UAV Applications and Optimization · Anomaly Detection Techniques and Applications
