Supportive Information to Find Victims from Aerial Video in Search and Rescue Operation An unmanned aerial vehicle (UAV) is expected to be one of the powerful IoT tools in a search and rescue (SAR) operation. Notably, an aerial video would be most promising. However, in a SAR operation, a searching task via an aerial video compels laborious tasks such as high concentration to practitioners. In this paper, to reduce such a laborious task, we propose a method to provide supportive information, i.e., the number of persons, the time and thumbnails, for finding victims via an aerial video with an object detection algorithm. Also, to use it in a SAR operation, we need to meet the requirements for a dataset and lightweight process. Through the evaluation, we showed that we could get supportive information reasonably and displayed them on a GUI.

Informed Region Selection for Efficient UAVbased Object Detectors: Altitudeaware Vehicle Detection with CyCAR Dataset Deep Learningbased object detectors enhance the capabilities of remote sensing platforms, such as Unmanned Aerial Vehicles (UAVs), in a wide spectrum of machine vision applications. However, the integration of deep learning introduces heavy computational requirements, preventing the deployment of such algorithms in scenarios that impose lowlatency constraints during inference, in order to make missioncritical decisions in realtime. In this paper, we address the challenge of efficient deployment of regionbased object detectors in aerial imagery, by introducing an informed methodology for extracting candidate detection regions (proposals). Our approach considers information from the UAV onboard sensors, such as flying altitude and lightweight computer vision filters, along with prior domain knowledge to intelligently decrease the number of region proposals by eliminating falsepositives at an early stage of the computation, reducing significantly the computational workload while sustaining the detection accuracy. We apply and evaluate the proposed approach on the task of vehicle detection. Our experiments demonstrate that stateoftheart detection models can achieve up to 2.6x faster inference by employing our altitudeaware datadriven methodology. Alongside, we introduce and provide to the community a novel vehicleannotated and altitudestamped dataset of real UAV imagery, captured at numerous flying heights under a wide span of traffic scenarios.