Finding 3D Positions of Distant Objects from Noisy Camera Movement and
  Semantic Segmentation Sequences
Authors: name: Julius Pesonen, name: Arno Solin, name: Eija Honkavaara

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 dense depth estimation 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 using particle filters for both single and multiple
target scenarios. The method was studied using a 3D simulation and a
drone-based image segmentation sequence with global navigation satellite system
(GNSS)-based camera pose estimates. The results showed that a particle filter
can be used to solve practical localisation tasks based on camera poses and
image segments in these situations where other solutions fail. The particle
filter is independent of the detection method, making it flexible for new
tasks. The study also demonstrates that drone-based wildfire monitoring can be
conducted using the proposed method paired with a pre-existing image
segmentation model.