期刊
IEEE TRANSACTIONS ON MULTIMEDIA
卷 24, 期 -, 页码 2069-2083出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMM.2021.3075566
关键词
Head; Social factors; Human factors; Feature extraction; Real-time systems; Object detection; COVID-19; UAV; COVID-19; pedestrian detection; spatial attention; social distancing monitoring
资金
- National Key Research and Development Program of China [2018YFB2100501]
- Key Research and Development Program of Yunnan province in China [2018IB023]
- National Natural Science Foundation of China [42090012, 41771452, 41771454, 41901340]
- Consulting research project of Chinese Academy of Engineering [2020ZD16]
This article proposes a method for social distance monitoring using UAV, which utilizes a lightweight pedestrian detection network to detect pedestrians in real-time and calculate the social distance between them. Experimental results show that the proposed method outperforms traditional models on different datasets and enables real-time detection.
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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