Smart City Gnosys

Smart city article details

Title	Real-Time Video Anonymization In Smart City Intersections
ID_Doc	44483
Authors	Angus A.; Duan Z.; Zussman G.; Kostic Z.
Year	2022
Published	Proceedings - 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2022
DOI	http://dx.doi.org/10.1109/MASS56207.2022.00078
Abstract	Video cameras in smart cities can be used to provide data to improve pedestrian safety and traffic management. Video recordings inherently violate privacy, and technological solutions need to be found to preserve it. Smart city applications deployed on top of the COSMOS research testbed in New York City are envisioned to be privacy friendly. This contribution presents one approach to privacy preservation - a video anonymization pipeline implemented in the form of blurring of pedestrian faces and vehicle license plates. The pipeline utilizes customized deep-learning models based on YOLOv4 for detection of privacy-sensitive objects in street-level video recordings. To achieve real time inference, the pipeline includes speed improvements via NVIDIA TensorRT optimization. When applied to the video dataset acquired at an intersection within the COSMOS testbed in New York City, the proposed method anonymizes visible faces and license plates with recall of up to 99% and inference speed faster than 100 frames per second. The results of a comprehensive evaluation study are presented. A selection of anonymized videos can be accessed via the COSMOS testbed portal. © 2022 IEEE.
Author Keywords	Deep Learning; Object Detection; Privacy Protection; Sensors; Smart City; TensorRT; Video Surveillance

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