ArticleIEEE transactions on multimedia2022
Real-Time and Accurate UAV Pedestrian Detection for Social Distancing Monitoring in COVID-19 Pandemic.
Article in IEEE transactions on multimedia, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- LFC-YOLO: A Lightweight Feature-Complementary YOLO Framework for Small Object Detection in UAV-Based Visual Sensing.Sensors (Basel, Switzerland) · 2026Article
- Dense-stream YOLOv8n: a lightweight framework for real-time crowd monitoring in smart libraries.Scientific reports · 2025Article
- Novel Surveillance View: A Novel Benchmark and View-Optimized Framework for Pedestrian Detection from UAV Perspectives.Sensors (Basel, Switzerland) · 2025Article
- Automatic zoning for retinopathy of prematurity with a key area location system.Biomedical optics express · 2024Article
- Unmanned aerial vehicles and pre-hospital emergency medicine.Scandinavian journal of trauma, resuscitation and emergency medicine · 2024Review
- Risk factors for transmission in a COVID-19 cluster infection in a high school in the Republic of Korea.Osong public health and research perspectives · 2023Article
- COVID-19 contact tracking by group activity trajectory recovery over camera networks.Pattern recognition · 2022Article
- Deep visual social distancing monitoring to combat COVID-19: A comprehensive survey.Sustainable cities and society · 2022Review
- Role of Drone Technology Helping in Alleviating the COVID-19 Pandemic.Micromachines · 2022Review
- Crowd dynamics research in the era of Covid-19 pandemic: Challenges and opportunities.Safety science · 2022Article
- STROVE: spatial data infrastructure enabled cloud-fog-edge computing framework for combating COVID-19 pandemic.Innovations in systems and software engineering · 2022Article
- A Social Distance Estimation and Crowd Monitoring System for Surveillance Cameras.Sensors (Basel, Switzerland) · 2022Article
- COVID-19 Isolation Control ProposalFrontiers in public health · 2022Article
- ATS-LIA: A lightweight mutual authentication based on adaptive trust strategy in flying ad-hoc networks.Peer-to-peer networking and applications · 2022Article
- Monitoring social-distance in wide areas during pandemics: a density map and segmentation approach.Applied intelligence (Dordrecht, Netherlands) · 2022Article
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6 authors.
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Abstract
Coronavirus Disease 2019 (COVID-19) is a highly infectious virus that has created a health crisis for people all over the world. Social distancing has proved to be an effective non-pharmaceutical measure to slow down the spread of COVID-19. As unmanned aerial vehicle (UAV) is a flexible mobile platform, it is a promising option to use UAV for social distance monitoring. Therefore, we propose a lightweight pedestrian detection network to accurately detect pedestrians by human head detection in real-time and then calculate the social distancing between pedestrians on UAV images. In particular, our network follows the PeleeNet as backbone and further incorporates the multi-scale features and spatial attention to enhance the features of small objects, like human heads. The experimental results on Merge-Head dataset show that our method achieves 92.22% AP (average precision) and 76 FPS (frames per second), outperforming YOLOv3 models and SSD models and enabling real-time detection in actual applications. The ablation experiments also indicate that multi-scale feature and spatial attention significantly contribute the performance of pedestrian detection. The test results on UAV-Head dataset show that our method can also achieve high precision pedestrian detection on UAV images with 88.5% AP and 75 FPS. In addition, we have conducted a precision calibration test to obtain the transformation matrix from images (vertical images and tilted images) to real-world coordinate. Based on the accurate pedestrian detection and the transformation matrix, the social distancing monitoring between individuals is reliably achieved.
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