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1. Real-timeMulti-CameraAnalytics forTrafficInformationExtractionandVisualization

   MahshidGhasemi†, SofiaKleisarchaki‡ ( January 2023 , IEEE PerCom'23)

   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. Index Terms—object detection and tracking, camera networks, smart intersection, real-time visualization 

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2. Demo: Real-time Multi-Camera Analytics for Traffic Information Extraction and Visualization

   M. Ghasemi, S. Kleisarchaki ( January 2023 , in Proc. IEEE PerCom’23, Mar. 2023)

   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. 

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3. Bird's-eye View Social Distancing Analysis System

   https://doi.org/10.1109/ICCWorkshops53468.2022.9814627  

   Yang, Zhengye ; Sun, Mingfei ; Ye, Hongzhe ; Xiong, Zihao ; Zussman, Gil ; Kostic, Zoran ( May 2022 , in Proc. IEEE ICC 2022 Workshop on Edge Learning for 5G Mobile Networks and Beyond, 2022)

   Social distancing can reduce the infection rates in respiratory pandemics such as COVID-19. Traffic intersections are particularly suitable for monitoring and evaluation of social distancing behavior in metropolises. Hence, in this paper, we propose and evaluate a real-time privacy-preserving social distancing analysis system (B-SDA), which uses bird’s-eye view video recordings of pedestrians who cross traffic intersections. We devise algorithms for video pre-processing, object detection, and tracking which are rooted in the known computer-vision and deep learning techniques, but modified to address the problem of detecting very small objects/pedestrians captured by a highly elevated camera. We propose a method for incorporating pedestrian grouping for detection of social distancing violations, which achieves 0.92 F1 score. B-SDA is used to compare pedestrian behavior in pre-pandemic and during-pandemic videos in uptown Manhattan, showing that the social distancing violation rate of 15.6% during the pandemic is notably lower than 31.4% prepandemic baseline. Keywords—Social distancing, Object detection, Smart city, Testbeds 

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4. Smart city intersections: Intelligence nodes for future metropolises

   Z. Kostic, A. Angus ( May 2022 , Cornell University Library)

   Traffic intersections are the most suitable locations for the deployment of computing, communications, and intelligence services for smart cities of the future. The abundance of data to be collected and processed, in combination with privacy and security concerns, motivates the use of the edgecomputing paradigm which aligns well with physical intersections in metropolises. This paper focuses on high-bandwidth, lowlatency applications, and in that context it describes: (i) system design considerations for smart city intersection intelligence nodes; (ii) key technological components including sensors, networking, edge computing, low latency design, and AI-based intelligence; and (iii) applications such as privacy preservation, cloud-connected vehicles, a real-time ”radar-screen”, traffic management, and monitoring of pedestrian behavior during pandemics. The results of the experimental studies performed on the COSMOS testbed located in New York City are illustrated. Future challenges in designing human-centered smart city intersections are summarized. 

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5. Smart city intersections: Intelligence nodes for future metropolises

   Z. Kostic, A. Angus ( January 2022 , arXiv:2205.01686v2 [cs.CV], May 2022.)

   Traffic intersections are the most suitable locations for the deployment of computing, communications, and intelligence services for smart cities of the future. The abundance of data to be collected and processed, in combination with privacy and security concerns, motivates the use of the edgecomputing paradigm which aligns well with physical intersections in metropolises. This paper focuses on high-bandwidth, lowlatency applications, and in that context it describes: (i) system design considerations for smart city intersection intelligence nodes; (ii) key technological components including sensors, networking, edge computing, low latency design, and AI-based intelligence; and (iii) applications such as privacy preservation, cloud-connected vehicles, a real-time ”radar-screen”, traffic management, and monitoring of pedestrian behavior during pandemics. The results of the experimental studies performed on the COSMOS testbed located in New York City are illustrated. Future challenges in designing human-centered smart city intersections are summarized. 

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