Automatic estimation of optimal UAV flight parameters for real-time wide areas monitoring

In the last few years, small-scale Unmanned Aerial Vehicles (UAVs) have been used in many video-based monitoring applications, such as search and rescue (SAR) operations, border control, precision agriculture, and many others. Usually, during these missions, a human operator manually selects UAV flight parameters according to the specific monitoring application to be performed. Anyway, regardless a particular mission, some main tasks can be considered common preprocessing steps that require to be accomplished. These tasks include the mosaicking of areas of interest, the detection of changes over time on these areas, finally, the classification of what is present on the ground. The success of these tasks strictly depends on flight and video sensor parameters. In this paper, for the first time in the literature, a method to automatically estimate the optimal parameters, in particular altitude and frame rate, to accomplish the three main tasks reported above is presented and tested. The parameters are estimated according to several factors, including size of the target to be analysed, cruise speed of UAVs, and main internal parameters of the video sensor, i.e., focal length, field of view, and size of the pixel. The full effectiveness of the proposed method, on the three case studies (i.e., main tasks), was proven both by synthetic videos generated with the Aerial Informatics and Robotics Simulation (AirSim) and by real video sequences reported in the UAV Mosaicking and Change Detection (UMCD) and NPU Drone-Map datasets.

Automated summary

The study proposes a method to automatically estimate UAV monitoring parameters, successfully applied to tasks including area mosaicking, change detection over time, and ground content classification. Parameter estimation involves factors such as target size, UAV cruise speed, and internal parameters of the video sensor.