從噪聲相機運動與語義分割序列中推斷遠距離物體的三維位置

摘要

基於相機測量序列的三維物體定位對於安全關鍵的監控任務至關重要，例如基於無人機的野火監測。通常，利用相機檢測到的物體定位可以通過密集深度估計或三維場景重建來解決。然而，在遠距離物體或受可用計算資源限制的任務背景下，這兩種方案都不可行。本文中，我們展示了該任務可以通過粒子濾波器來解決，適用於單目標和多目標場景。該方法通過三維模擬和基於全球導航衛星系統（GNSS）的相機姿態估計的無人機圖像分割序列進行了研究。結果表明，在這些其他解決方案失效的情況下，粒子濾波器可以基於相機姿態和圖像分割來解決實際的定位任務。粒子濾波器獨立於檢測方法，使其對新任務具有靈活性。該研究還證明了基於無人機的野火監測可以通過所提出的方法與現有的圖像分割模型相結合來進行。

English

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.

從噪聲相機運動與語義分割序列中推斷遠距離物體的三維位置

Finding 3D Positions of Distant Objects from Noisy Camera Movement and Semantic Segmentation Sequences

摘要

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