Finding 3D Positions of Distant Objects from Noisy Camera Movement and
  Semantic Segmentation Sequences
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.
68da4dadcfa03a5158a1ed08
Julius Pesonen
claimed_verified
2025-09-29T11:26:14.579Z
66964ee4417bbfcd51e3ce20
/avatars/705b5dd98890e4fb7ebd9b91c9d5dc38.svg
Julius Pesonen
user
Julppe1
68da4dadcfa03a5158a1ed09
Arno Solin
68da4dadcfa03a5158a1ed0a
Eija Honkavaara
Finding 3D Positions of Distant Objects from Noisy Camera Movement and
  Semantic Segmentation Sequences
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.
Particle filters enable 3D object localization using camera poses and image segments, suitable for drone-based surveillance with limited computational resources.
particle filters 3D object localization camera measurements dense depth estimation 3D scene reconstruction GNSS-based camera pose estimates image segmentation drone-based wildfire monitoring