UAV-Based Human Body Detector Selection and Fusion for Geolocated Saliency Map Generation
<|reference_start|>UAV-Based Human Body Detector Selection and Fusion for Geolocated Saliency Map Generation: The problem of reliably detecting and geolocating objects of different classes in soft real-time is essential in many application areas, such as Search and Rescue performed using Unmanned Aerial Vehicles (UAVs). This research addresses the complementary problems of system contextual vision-based detector selection, allocation, and execution, in addition to the fusion of detection results from teams of UAVs for the purpose of accurately and reliably geolocating objects of interest in a timely manner. In an offline step, an application-independent evaluation of vision-based detectors from a system perspective is first performed. Based on this evaluation, the most appropriate algorithms for online object detection for each platform are selected automatically before a mission, taking into account a number of practical system considerations, such as the available communication links, video compression used, and the available computational resources. The detection results are fused using a method for building maps of salient locations which takes advantage of a novel sensor model for vision-based detections for both positive and negative observations. A number of simulated and real flight experiments are also presented, validating the proposed method.<|reference_end|>
@article{rudol2024uav-based,
    title={UAV-Based Human Body Detector Selection and Fusion for Geolocated
  Saliency Map Generation},
    author={Piotr Rudol, Patrick Doherty, Mariusz Wzorek, Chattrakul Sombattheera},
    journal={arXiv preprint arXiv:2408.16501},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16501},
    primaryClass={cs.CV cs.RO}
}