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 avoids explicit localization. GD3A establishes pixel-level correspondences between pedestrian descriptors across consecutive frames via optimal transport with an adaptive dustbin score, enabling the decomposition of global density maps into shared, inflow, and outflow components. Building on this framework, we further introduce DVTrack, which converts descriptor-level matching into instance-level associations through a descriptor voting mechanism for pedestrian tracking. Experimental results show that our methods significantly outperform existing approaches under dense crowds and complex motion, reducing counting error by 47.4 percent and improving tracking performance by 39.2 percent.
Abstract (translated)
在大规模场景中计数和追踪密集人群是一项极具挑战性的任务，但现有的方法主要依赖于固定摄像头捕获的数据集，这些数据集提供的空间覆盖范围有限，并不足以进行大规模密集人群的分析。为了克服这一限制，我们提出了一种灵活的解决方案，即使用移动无人机来捕捉视频并执行整个场景内的视频级人群计数和独特的行人人身追踪。为此，我们引入了MovingDroneCrowd++，这是一个由移动无人机捕获的最大规模视频级数据集，用于密集人群计数和跟踪，涵盖了各种复杂条件，包括变化的飞行高度、相机角度以及光照条件。现有方法在该数据集中未能达到满意的性能表现。 为了解决这个问题，我们提出了GD3A（通过描述符关联进行全局密度图分解），这是一种基于密度图的视频个体计数方法，避免了显式的定位步骤。GD3A利用最优传输和自适应尘盒得分，在连续帧之间建立行人的描述符像素级对应关系，从而将全局密度图分解为共享、流入以及流出成分。 在此框架基础上，我们进一步引入了DVTrack，通过描述符投票机制将描述符级别的匹配转换为实例级别的关联，以实现行人追踪。实验结果表明，我们的方法在密集人群和复杂运动条件下显著优于现有方法，在计数误差上减少了47.4%，并提高了39.2%的跟踪性能。
URL
https://arxiv.org/abs/2601.12500
