Moving People Tracking and False Track Removing with Infrared Thermal Imaging by a Multirotor

Use of Unmanned Aerial Vehicles in Wilderness Search and Rescue Operations: A Scoping Review

Wilderness Search and Rescue (WSAR) focuses on locating and extricating missing persons in remote settings. As unmanned aerial vehicle (UAV) or "drone" technology has evolved, so has the literature describing its application in WSAR operations. We conducted a scoping review of literature that describes the use of UAVs in WSAR contexts. The Joanna Briggs Institute Framework for scoping reviews was followed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews method. Additional individual databases, article reference lists, and relevant grey literature were also included in the search to provide an impartial scope. Seven hundred forty-seven articles were identified. Of these, 56 were found to be duplicates. The remaining 691 were further screened and checked for eligibility. Ultimately, 21 studies were found that met our inclusion criteria. This literature supports the use of UAVs to increase the safety and efficiency of a WSAR operation for locating victims, assessing risks, carrying equipment, and restoring communication systems. Unmanned aerial vehicles are a potentially useful adjunct in the management of WSAR operations. Their limitations include objects obscuring victims, weather changes, uneven terrain, battery-limited flight time, and susceptibility to environmental damage.

Mission Chain Driven Unmanned Aerial Vehicle Swarms Cooperation for the Search and Rescue of Outdoor Injured Human Targets

A novel cooperative strategy for distributed unmanned aerial vehicle (UAV) swarms with different functions, namely the mission chain-driven unmanned aerial vehicle swarms cooperation method, is proposed to allow the fast search and timely rescue of injured human targets in a wide-area outdoor environment. First, a UAV-camera unit is exploited to detect the suspected human target combined with improved deep learning technology. Then, the target location information is transferred to a self-organizing network. Then, the special bio-radar-UAV unit was released to recheck the survivals through a respiratory characteristic detection algorithm. Finally, driven by the location and vital sign status of the injured, a nearby emergency-UAV unit will perform corresponding medical emergency missions, such as dropping emergency supplies. Experimental results show that this strategy can identify the human targets autonomously from the outdoor environment effectively, and the target detection, target sensing, and medical emergency mission chain is completed successfully relying on the cooperative working mode, which is meaningful for the future search-rescue mission of outdoor injured human targets.

Long Distance Ground Target Tracking with Aerial Image-to-Position Conversion and Improved Track Association

A small drone is capable of capturing distant objects at a low cost. In this paper, long distance (up to 1 km) ground target tracking with a small drone is addressed for oblique aerial images, and two novel approaches are developed. First, the coordinates of the image are converted to real-world based on the angular field of view, tilt angle, and altitude of the camera. Through the image-to-position conversion, the threshold of the actual object size and the center position of the detected object in real-world coordinates are obtained. Second, the track-to-track association is improved by adopting the nearest neighbor association rule to select the fittest track among multiple tracks in a dense track environment. Moving object detection consists of frame-to-frame subtraction and thresholding, morphological operation, and false alarm removal based on object size and shape properties. Tracks are initialized by differencing between the two nearest points in consecutive frames. The measurement statistically nearest to the state prediction updates the target’s state. With the improved track-to-track association, the fittest track is selected in the track validation region, and the direction of the displacement vector and velocity vectors of the two tracks are tested with an angular threshold. In the experiment, a drone hovered at an altitude of 400 m capturing video for about 10 s. The camera was tilted 30° downward from the horizontal. Total track life (TTL) and mean track life (MTL) were obtained for 86 targets within approximately 1 km of the drone. The interacting multiple mode (IMM)-CV and IMM-CA schemes were adopted with varying angular thresholds. The average TTL and MTL were obtained as 84.9–91.0% and 65.6–78.2%, respectively. The number of missing targets was 3–5; the average TTL and MTL were 89.2–94.3% and 69.7–81.0% excluding the missing targets.