3D object localisation based on a sequence of camera measurements is essential for safety-critical surveillance tasks, such as drone-based wildfire monitoring. Localisation of objects detected with a camera can typically be solved with specialised sensor configurations or 3D scene reconstruction. However, in the context of distant objects or tasks limited by the amount of available computational resources, neither solution is feasible. In this paper, we show that the task can be solved with either multi-view triangulation or particle filters, with the latter also providing shape and uncertainty estimates. We studied the solutions using 3D simulation and drone-based image segmentation sequences with global navigation satellite system (GNSS) based camera pose estimates. The results suggest that combining the proposed methods with pre-existing image segmentation models and drone-carried computational resources yields a reliable system for drone-based wildfire monitoring. The proposed solutions are independent of the detection method, also enabling quick adaptation to similar tasks. Code is available at https://fgi_nls.gitlab.io/public/distant-localisation

翻译：基于相机测量序列的三维目标定位对于安全关键型监控任务（如无人机林火监测）至关重要。利用相机检测到的目标定位通常可通过专用传感器配置或三维场景重建来解决。然而，在远距离目标或受可用计算资源限制的任务背景下，这两种方案均不可行。本文证明该任务可通过多视图三角测量或粒子滤波解决，其中后者还能提供形状和不确定性估计。我们通过三维仿真和基于全球导航卫星系统（GNSS）相机位姿估计的无人机图像分割序列研究了这些解决方案。结果表明，将所提方法与现有图像分割模型及无人机搭载的计算资源相结合，可构建可靠的无人机林火监测系统。所提方案与检测方法无关，同时能够快速适配类似任务。代码已开源：https://fgi_nls.gitlab.io/public/distant-localisation