Smart City Gnosys

Smart city article details

Title Real-Time Multi-Camera Analytics For Traffic Information Extraction And Visualization
ID_Doc 44405
Authors Ghasemi M.; Kleisarchaki S.; Calmant T.; Lu J.; Ojha S.; Kostic Z.; Gurgen L.; Zussman G.; Ghaderi J.
Year 2023
Published 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023
DOI http://dx.doi.org/10.1109/PerComWorkshops56833.2023.10150224
Abstract The density and complexity of urban environments present significant challenges for autonomous vehicles. Moreover, ensuring pedestrians' safety and protecting personal privacy are crucial considerations in these environments. Smart city intersections and AI-powered traffic management systems will be essential for addressing these challenges. Therefore, our research focuses on creating an experimental framework for the design of applications that support the secure and efficient management of traffic intersections in urban areas. We integrated two cameras (street-level and bird's eye view), both viewing an intersection, and a programmable edge computing node, deployed within the COSMOS testbed in New York City, with a central management platform provided by Kentyou. We designed a pipeline to collect and analyze the video streams from both cameras and obtain real-time traffic/pedestrian-related information to support smart city applications. The obtained information from both cameras is merged, and the results are sent to a dedicated dashboard for real-time visualization and further assessment (e.g., accident prevention). The process does not require sending the raw videos in order to avoid violating pedestrians' privacy. In this demo, we present the designed video analytic pipelines and their integration with Kentyou central management platform. © 2023 IEEE.
Author Keywords camera networks; object detection and tracking; real-time visualization; smart intersection


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