arXiv Computer Vision
Sep 7

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

The paper introduces MovingDroneCrowd++, a large-scale video dataset for dense crowd counting and tracking from moving drones, featuring varied flight altitudes, camera angles, and lighting. It presents two new methods: GD3A for Video Individual Counting and GIA-Track for Multi-Object Tracking, both leveraging group-wise density assignment and identity association to handle aerial challenges. Experiments demonstrate significant improvements, reducing counting error by 47.4% and boosting tracking accuracy by 64.6%.

By Yaowu Fan, Jia Wan, Tao Han, Andy J. Ma, Wanli Ouyang, Antoni B. Chan
arXiv Computer Vision
Sep 17

Understanding Dynamic Scenes at Gigapixel Scale: Wide-Area Spatio-Temporal Perception from UAVs

The paper introduces the Wide-area Spatio-temporal Scene Understanding (WSTU) problem, which demands simultaneous wide-area coverage, per-target resolution, and temporal continuity—capabilities lacking in existing datasets. To address this, the authors present HARD, an ultra‑high‑resolution (12768×9564) UAV dataset annotated for object detection, multi‑object tracking, and scene‑level visual question answering. They also propose a latency‑aware metric, streaming‑HOTA (s‑HOTA), and show through baseline experiments that high resolution and processing latency significantly impact detection, tracking, and VQA performance, revealing gaps in current methods for WSTU.

By Yuhang Zhu, Meiyi Zhu, Yunkai Dang, Zhangnan Li, Yuxuan Wang, Wenbin Li, Hongbing Pan