On the Role of Field of View for Occlusion Removal with Airborne Optical
Sectioning
- URL: http://arxiv.org/abs/2204.13371v1
- Date: Thu, 28 Apr 2022 09:26:10 GMT
- Title: On the Role of Field of View for Occlusion Removal with Airborne Optical
Sectioning
- Authors: Francis Seits, Indrajit Kurmi, Rakesh John Amala Arokia Nathan, Rudolf
Ortner, and Oliver Bimber
- Abstract summary: Occlusion caused by vegetation is an essential problem for remote sensing applications in areas.
Airborne Optical Sectioning (AOS) is an optical, wavelength-independent synthetic aperture imaging technique.
We demonstrate a relationship between forest density and field of view (FOV) of applied imaging systems.
- Score: 3.5232085374661284
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Occlusion caused by vegetation is an essential problem for remote sensing
applications in areas, such as search and rescue, wildfire detection, wildlife
observation, surveillance, border control, and others. Airborne Optical
Sectioning (AOS) is an optical, wavelength-independent synthetic aperture
imaging technique that supports computational occlusion removal in real-time.
It can be applied with manned or unmanned aircrafts, such as drones. In this
article, we demonstrate a relationship between forest density and field of view
(FOV) of applied imaging systems. This finding was made with the help of a
simulated procedural forest model which offers the consideration of more
realistic occlusion properties than our previous statistical model. While AOS
has been explored with automatic and autonomous research prototypes in the
past, we present a free AOS integration for DJI systems. It enables bluelight
organizations and others to use and explore AOS with compatible, manually
operated, off-the-shelf drones. The (digitally cropped) default FOV for this
implementation was chosen based on our new finding.
Related papers
- Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue
with Autonomous Heterogeneous Robotic Systems [56.838297900091426]
Smoke and dust affect the performance of any mobile robotic platform due to their reliance on onboard perception systems.
This paper proposes a novel modular computation filtration pipeline based on intensity and spatial information.
arXiv Detail & Related papers (2023-08-14T16:48:57Z) - Synthetic Aperture Anomaly Imaging [2.9443230571766854]
We show that integrating detected anomalies is even more effective than detecting anomalies in integrals.
We present a real-time application that makes our findings practically available for blue-light organizations and others using commercial drone platforms.
arXiv Detail & Related papers (2023-04-26T14:34:43Z) - Threatening Patch Attacks on Object Detection in Optical Remote Sensing
Images [55.09446477517365]
Advanced Patch Attacks (PAs) on object detection in natural images have pointed out the great safety vulnerability in methods based on deep neural networks.
We propose a more Threatening PA without the scarification of the visual quality, dubbed TPA.
To the best of our knowledge, this is the first attempt to study the PAs on object detection in O-RSIs, and we hope this work can get our readers interested in studying this topic.
arXiv Detail & Related papers (2023-02-13T02:35:49Z) - Inverse Airborne Optical Sectioning [4.640835690336653]
Inverse Airborne Optical Sectioning (IAOS) is an optical analogy to Inverse Synthetic Aperture Radar (ISAR)
Moving targets, such as walking people, that are heavily occluded by vegetation can be made visible and tracked with a stationary optical sensor.
arXiv Detail & Related papers (2022-07-27T07:57:24Z) - Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Images [96.66271207089096]
FCOS-LiDAR is a fully convolutional one-stage 3D object detector for LiDAR point clouds of autonomous driving scenes.
We show that an RV-based 3D detector with standard 2D convolutions alone can achieve comparable performance to state-of-the-art BEV-based detectors.
arXiv Detail & Related papers (2022-05-27T05:42:16Z) - DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly
detection in air transportation [68.8204255655161]
We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE)
It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase.
Results show that the DAE achieves better results in both accuracy and speed of detection.
arXiv Detail & Related papers (2021-09-08T14:07:55Z) - A Multi-UAV System for Exploration and Target Finding in Cluttered and
GPS-Denied Environments [68.31522961125589]
We propose a framework for a team of UAVs to cooperatively explore and find a target in complex GPS-denied environments with obstacles.
The team of UAVs autonomously navigates, explores, detects, and finds the target in a cluttered environment with a known map.
Results indicate that the proposed multi-UAV system has improvements in terms of time-cost, the proportion of search area surveyed, as well as successful rates for search and rescue missions.
arXiv Detail & Related papers (2021-07-19T12:54:04Z) - An Autonomous Drone for Search and Rescue in Forests using Airborne
Optical Sectioning [0.0]
We present a first prototype that finds people fully autonomously in densely occluded forests.
In the course of 17 field experiments conducted over various forest types, our drone found 38 out of 42 hidden persons.
Deep-learning-based person classification is unaffected by sparse and error-prone sampling within one-dimensional synthetic apertures.
arXiv Detail & Related papers (2021-05-10T13:05:22Z) - Multi-Agent Active Search using Realistic Depth-Aware Noise Model [8.520962086877548]
Active search for objects of interest in an unknown environment has many robotics applications including search and rescue, detecting gas leaks or locating animal poachers.
Existing algorithms often prioritize the location accuracy of objects of interest while other practical issues such as the reliability of object detection as a function of distance and lines of sight remain largely ignored.
We present an algorithm called Noise-Aware Thompson Sampling (NATS) that addresses these issues for multiple ground-based robots performing active search considering two sources of sensory information from monocular optical imagery and depth maps.
arXiv Detail & Related papers (2020-11-09T23:20:55Z) - Search and Rescue with Airborne Optical Sectioning [7.133136338850781]
We show that automated person detection can be significantly improved by combining multi-perspective images before classification.
Findings lay the foundation for effective future search and rescue technologies that can be applied in combination with autonomous or manned aircraft.
arXiv Detail & Related papers (2020-09-18T13:40:19Z) - Perceiving Traffic from Aerial Images [86.994032967469]
We propose an object detection method called Butterfly Detector that is tailored to detect objects in aerial images.
We evaluate our Butterfly Detector on two publicly available UAV datasets (UAVDT and VisDrone 2019) and show that it outperforms previous state-of-the-art methods while remaining real-time.
arXiv Detail & Related papers (2020-09-16T11:37:43Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.