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Smart city article details

Title	Real-Time Camera Analytics For Enhancing Traffic Intersection Safety
ID_Doc	44316
Authors	Ghasemi M.; Kleisarchaki S.; Calmant T.; Gürgen L.; Ghaderi J.; Kostic Z.; Zussman G.
Year	2022
Published	MobiSys 2022 - Proceedings of the 2022 20th Annual International Conference on Mobile Systems, Applications and Services
DOI	http://dx.doi.org/10.1145/3498361.3538669
Abstract	Crowded metropolises present unique challenges to the potential deployment of autonomous vehicles. Safety of pedestrians cannot be compromised and personal privacy must be preserved. Smart city intersections will be at the core of Artificial Intelligence (AI)-powered citizen-friendly traffic management systems for such metropolises. Hence, the main objective of this work is to develop an experimentation framework for designing applications in support of secure and efficient traffic intersections in urban areas. We integrated a camera and a programmable edge computing node, deployed within the COSMOS testbed in New York City, with an Eclipse sensiNact data platform provided by Kentyou. We use this pipeline to collect and analyze video streams in real-time to support smart city applications. In this demo, we present a video analytics pipeline that analyzes the video stream from a COSMOS' street-level camera to extract traffic/crowd-related information and sends it to a dedicated dashboard for real-time visualization and further assessment. This is done without sending the raw video, in order to avoid violating pedestrians' privacy. © 2022 Owner/Author.
Author Keywords	camera networks; object detection; smart intersection

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