Finding 3D Positions of Distant Objects from Noisy Camera Movement and Semantic Segmentation Sequences
This addresses safety-critical surveillance problems like wildfire monitoring for drone operators, but it is incremental as it applies existing particle filter methods to a specific domain.
The paper tackled 3D object localization from noisy camera movements and semantic segmentation sequences, showing that particle filters can solve practical tasks where dense depth estimation or 3D reconstruction fail, as demonstrated in drone-based wildfire monitoring simulations.
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.