移动无人机的视频个体计数
Video Individual Counting for Moving Drones
AI总结：
提出 MovingDroneCrowd 数据集和 SDNet 网络，通过深度跨帧注意力模块估计共享密度图并推导流入流出密度图，解决移动无人机在拥挤场景中的视频个体计数问题。
摘要：
视频个体计数（VIC）因其在智能视频监控中的重要性而受到越来越多的关注。现有工作在数据集和方法两个方面存在局限。以往的数据集由固定或极少移动的摄像头采集，个体相对稀疏，限制了对拥挤场景中高度变化的视角和时间的评估。现有方法依赖定位后进行关联或分类，由于小目标定位不准确，在密集和动态条件下表现不佳。为解决这些问题，我们引入了 MovingDroneCrowd Dataset，其特点是在不同光照、拍摄高度和角度下，由快速移动的无人机在拥挤场景中捕获的视频。我们进一步提出了一种共享密度图引导网络（SDNet），使用深度跨帧注意力（DCFA）模块直接估计连续帧之间的共享密度图，通过从全局密度图中减去共享密度图，推导出流入和流出密度图。将跨帧的流入密度图相加，得到视频中独特行人的数量。在我们的数据集和公开数据集上的实验表明，我们的方法在高度动态和复杂的拥挤场景中优于现有技术。我们的数据集和代码已公开发布。
英文摘要：
Video Individual Counting (VIC) has received increasing attention for its importance in intelligent video surveillance. Existing works are limited in two aspects, i.e., dataset and method. Previous datasets are captured with fixed or rarely moving cameras with relatively sparse individuals, restricting evaluation for a highly varying view and time in crowded scenes. Existing methods rely on localization followed by association or classification, which struggle under dense and dynamic conditions due to inaccurate localization of small targets. To address these issues, we introduce the MovingDroneCrowd Dataset, featuring videos captured by fast-moving drones in crowded scenes under diverse illuminations, shooting heights and angles. We further propose a Shared Density map-guided Network (SDNet) using a Depth-wise Cross-Frame Attention (DCFA) module to directly estimate shared density maps between consecutive frames, from which the inflow and outflow density maps are derived by subtracting the shared density maps from the global density maps. The inflow density maps across frames are summed up to obtain the number of unique pedestrians in a video. Experiments on our datasets and publicly available ones show the superiority of our method over the state of the arts in highly dynamic and complex crowded scenes. Our dataset and codes have been released publicly.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
