Wide Synthetic Aperture Sampling for Motion Classification

Considering the current high level of attention that is being paid to drones in connection with their military uses, it is easy to overlook the enormous potential that they bring with them in civilian areas. Drone groups are establishing themselves worldwide in blue light organizations such as the police, fire brigade and mountain rescue to use this technology to save human lives. Search and rescue operations benefit, among other things, from the flexible, fast and - compared to helicopters - inexpensive and safe use of drones. They are also used in the inspection of disaster areas, for the early detection of forest fires, for border security, or wildlife observation. The problem with all these applications is always the occlusion caused by vegetation, such as forest, which usually makes it impossible to find, detect, and track people, animals or vehicles in single aerial photographs. The "Airborne Optical Sectioning" (AOS) imaging method developed at the Johannes Kepler University solves this problem with a special scanning principle. Similar to the networking of radio telescopes distributed around the world to improve measurement signals, AOS combines several images recorded over a large area in order to computationally remove occlusion in real time. This creates a largely unobstructed view of the forest floor. Because AOS combines frames that are captured one after the other during flight, it has been difficult so far to detect fast movements, such as walking people or animals, under dense forest. This problem is now to be examined in particular by this new basic research project, which is jointly financed by the German Research Foundation (DFG) and the Austrian Science Fund (FWF). In addition to the Johannes Kepler University in Linz, the German Aerospace Center (DLR) in Oberpfaffenhofen and the Otto von Guericke University in Magdeburg are also involved. One focus of this project is, among other things, the use of autonomous drone swarms, which collectively contribute to solving this problem. Here, drones can, for example, imitate the swarming behavior of birds in order to always have an optimal view of the object to be found (e.g. a person). However, together they generate the optical signal of a very large, adaptable lens many meters in diameter. The shallow depth of field of this optical signal makes the forest disappear. The dynamic behavior of the swarm now also allows it to react to the movements of the object. So if a person is walking in dense forest, the swarm can find him, make him visible, and follow him, despite being heavily concealed. As part of the new basic research project, such approaches are now to be implemented with real swarms of drones, tested in field studies and further developed. In the future, swarms of autonomous drones will be able to search for missing people or count wildlife populations. Collectively, a swarm can of course act much faster and farther than a single drone.

Considering the current high level of attention that is being paid to drones in connection with their military uses, it is easy to overlook the enormous potential that they bring with them in civilian areas. Drone groups are establishing themselves worldwide in blue light organizations such as the police, fire brigade and mountain rescue to use this technology to save human lives. Search and rescue operations benefit, among other things, from the flexible, fast and - compared to helicopters - inexpensive and safe use of drones. They are also used in the inspection of disaster areas, for the early detection of forest fires, for border security, or wildlife observation. The problem with all these applications is always the occlusion caused by vegetation, such as forest, which usually makes it impossible to find, detect, and track people, animals or vehicles in single aerial photographs. The "Airborne Optical Sectioning" (AOS) imaging method developed at the Johannes Kepler University solves this problem with a special scanning principle. Similar to the networking of radio telescopes distributed around the world to improve measurement signals, AOS combines several images recorded over a large area in order to computationally remove occlusion in real time. This creates a largely unobstructed view of the forest floor. Because AOS combines frames that are captured one after the other during flight, it has been difficult so far to detect fast movements, such as walking people or animals, under dense forest. This problem was now examined in particular by this new basic research project, which is jointly financed by the German Research Foundation (DFG) and the Austrian Science Fund (FWF). In addition to the Johannes Kepler University in Linz, the German Aerospace Center (DLR) in Oberpfaffenhofen and the Otto von Guericke University Magdeburg were also involved. One focus of this project was, among other things, the use of autonomous drone swarms, which collectively contribute to solving this problem. Here, drones can, for example, imitate the swarming behavior of birds in order to always have an optimal view of the object to be found (e.g. a person). However, together they generate the optical signal of a very large, adaptable lens many meters in diameter. The shallow depth of field of this optical signal makes the forest disappear. The dynamic behavior of the swarm now also allows it to react to the movements of the object. So if a person is walking in dense forest, the swarm can find him, make him visible, and follow him, despite being heavily concealed. As part of this basic research project, such approaches were implemented with real swarms of drones, tested in field studies and further developed.