UAV-Based Human Body Detector Selection and Fusion for Geolocated Saliency Map Generation

• URL: http://arxiv.org/abs/2408.16501v1
• Date: Thu, 29 Aug 2024 13:00:37 GMT
• Title: UAV-Based Human Body Detector Selection and Fusion for Geolocated Saliency Map Generation
• Authors: Piotr Rudol, Patrick Doherty, Mariusz Wzorek, Chattrakul Sombattheera,
• Abstract summary: 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. 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.
• Score: 0.2499907423888049
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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