George Nousias | Searcharxiv

arXiv2026

Efficient UAV Coverage in Large Convex Quadrilateral Areas with Elliptical Footprints

Unmanned Aerial Vehicles (UAVs) have gained significant attention for improving wireless communication, especially in emergencies or as a complement to existing cellular infrastructure. This letter addresses the problem of efficiently covering a large convex quadrilateral using multiple UAVs, where each UAV generates elliptical coverage footprints based on its altitude and antenna tilt. The challenge is approached using circle-packing techniques within a unit square to arrange UAVs in an optimal configuration. Subsequently, a homography transformation is applied to map the unit square onto the quadrilateral area, ensuring that the UAVs' elliptical footprints cover the entire region. Numerical simulations demonstrate the effectiveness of the proposed method, providing insight into coverage density and optimal altitude configurations for different placement scenarios. The results highlight the scalability and potential for improving UAV-based communication systems, focusing on maximizing coverage efficiency in large areas with irregular shapes.

eess.SP↗

arXiv2025

Intelligent Sampling Consensus for Homography Estimation in Football Videos Using Featureless Unpaired Points

Estimating the homography matrix between images captured under radically different camera poses and zoom factors is a complex challenge. Traditional methods rely on the Random Sample Consensus (RANSAC) algorithm, which requires pairs of homologous points, pre-matched based on local image feature vectors. Sampling consensus is a core step in many Artificial Intelligence (AI) algorithms that enable computer systems to recognize patterns in data. In this paper, we propose H-RANSAC, an algorithm for homography estimation that eliminates the need for feature vectors or explicit point pairing, while it optionally supports point labeling into two classes. H-RANSAC introduces a novel geometric (cheiral) criterion that intelligently rejects implausible point configurations at the beginning of each iteration, while leveraging concave quadrilaterals typically discarded by similar algorithms. A post-hoc criterion at the end of each iteration improves accuracy further. Analytical derivations of the expected maximum iterations are provided, considering success probabilities and outlier rates, enabling adaptive performance tuning. The algorithm is validated on a demanding task: estimating homography between video frames of football matches captured by 12 cameras with highly divergent viewpoints. Results show that H-RANSAC significantly outperforms state-of-the-art classical methods, combined with deep learning-based salient point detection, in terms of average reprojection error and success rates. The relevant implementation is available in https://github.com/gnousias/H-RANSAC.

cs.CV↗