Real-time and Accurate UAV Pedestrian Detection for Social Distancing Monitoring in COVID-19 Pandemic

2021 
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 IEEE
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