UAV-Based Human Body Detector Selection and Fusion for Geolocated Saliency Map Generation
This addresses the need for timely and accurate object detection in UAV-based systems, but it is incremental as it builds on existing detector fusion techniques with practical system considerations.
The research tackled the problem of selecting and fusing vision-based detectors on UAVs for reliable object geolocation in applications like Search and Rescue, resulting in a validated method through simulated and real flight experiments.
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.