id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-258170-kyztc1jp	Shorfuzzaman, Mohammad	Towards the sustainable development of smart cities through mass video surveillance: A response to the COVID-19 pandemic	2020-11-05		.txt	text/plain	5371	300	54	In particular, we make the following contributions: (a) A deep learning-based framework is presented for monitoring social distancing in the context of sustainable smart cities in an effort to curb the spread of COVID-19 or similar infectious diseases; (b) The proposed system leverages state-of-the-art, deep learning-based real-time object detection models for the detection of people in videos, captured with a monocular camera, to implement social distancing monitoring use cases; (c) A J o u r n a l P r e -p r o o f perspective transformation is presented, where the captured video is transformed from a perspective view to a bird's eye (top-down) view to identify the region of interest (ROI) in which social distancing will be monitored; (d) A detailed performance evaluation is provided to show the effectiveness of the proposed system on a video surveillance dataset.	./cache/cord-258170-kyztc1jp.txt	./txt/cord-258170-kyztc1jp.txt