利用高分辨率图像中的每一个像素增强无人机图像中的人员定位以实现更好的人群管理
Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images
AI总结：
本文提出一种点导向目标定位方法及Pixel Distill模块，利用高分辨率无人机图像逐像素提取空间信息，并发布UP-COUNT数据集，在DroneCrowd等数据集上验证了其在动态人群定位与计数任务中的优越性能。
摘要：
使用无人机进行准确的人员定位对于有效的人群管理至关重要，不仅在大规模活动和公共集会期间如此，在监测日常城市人群流动方面也是如此。传统的高分辨率无人机图像中的微小目标定位方法往往在精度和效率上面临局限，这主要源于图像缩放和滑动窗口技术的限制。为应对这些挑战，提出了一种专门针对点导向目标定位的新方法。结合该方法，引入了Pixel Distill模块，通过一次性提取单个像素的空间信息来增强高清晰度图像的处理。此外，还共享了一个名为UP-COUNT的新数据集，该数据集针对当代无人机应用进行了定制。它解决了无人机图像中的广泛挑战，例如图像采集过程中相机和物体同时移动的问题，推动了人群管理应用能力的发展。在所提出的数据集和常用的DroneCrowd数据集上对所提方法进行的综合评估表明，我们的方法优于现有方法，并凸显了其在基于无人机的人群目标定位任务中的有效性。这些改进显著提高了算法在真实场景中的适用性，使得在动态环境中对个体进行更可靠的定位和计数成为可能。
英文摘要：
Accurate people localisation using drones is crucial for effective crowd management, not only during massive events and public gatherings but also for monitoring daily urban crowd flow. Traditional methods for tiny object localisation using high-resolution drone imagery often face limitations in precision and efficiency, primarily due to constraints in image scaling and sliding window techniques. To address these challenges, a novel approach dedicated to point-oriented object localisation is proposed. Along with this approach, the Pixel Distill module is introduced to enhance the processing of high-definition images by extracting spatial information from individual pixels at once. Additionally, a new dataset named UP-COUNT, tailored to contemporary drone applications, is shared. It addresses a wide range of challenges in drone imagery, such as simultaneous camera and object movement during the image acquisition process, pushing forward the capabilities of crowd management applications. A comprehensive evaluation of the proposed method on the proposed dataset and the commonly used DroneCrowd dataset demonstrates the superiority of our approach over existing methods and highlights its efficacy in drone-based crowd object localisation tasks. These improvements markedly increase the algorithm's applicability to operate in real-world scenarios, enabling more reliable localisation and counting of individuals in dynamic environments.
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。
