Towards semantic context-aware drones for aerial scenes understanding

Abstract

Visual object tracking with unmanned aerial vehicles (UAVs) plays a central role in the aerial surveillance. Reliable object detection depends on many factors such as large displacements, occlusions, image noise, illumination and pose changes or image blur that may compromise the object labeling. The paper presents a proposal for a hybrid solution that adds semantic information to the video tracking processing: along with the tracked objects, the scene is completely depicted by data from places, natural features, or in general Points of Interest (POIs). Each scene from a video sequence is semantically described by ontological statements which, by inference, support the object identification which often suffers from some weakness in the object tracking methods. The synergy between the tracking methods and semantic technologies seems to bridge the object labeling gap, enhance the understanding of the situation awareness, as well as critical alarming situations.