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1. 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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2. Real-time camera analytics for enhancing traffic intersection safety

   https://doi.org/10.1145/3498361.3538669  

   Ghasemi, Mahshid ; Kleisarchaki, Sofia ; Calmant, Thomas ; Gürgen, Levent ; Ghaderi, Javad ; Kostic, Zoran ; Zussman, Gil ( June 2022 , in Proc. ACM MobiSys’22, 2022)

   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. 

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3. Real-Time Traffic Pattern Collection and Analysis Model for Intelligent Traffic Intersection

   https://doi.org/10.1109/EDGE.2018.00028  

   Sreekumar, Unnikrishnan Kizhakkemadam ; Devaraj, Revathy ; Li, Qi ; Liu, Kaikai ( July 2018 , 2018 IEEE International Conference on Edge Computing (EDGE))

   The traffic congestion hits most big cities in the world - threatening long delays and serious reductions in air quality. City and local government officials continue to face challenges in optimizing crowd flow, synchronizing traffic and mitigating threats or dangerous situations. One of the major challenges faced by city planners and traffic engineers is developing a robust traffic controller that eliminates traffic congestion and imbalanced traffic flow at intersections. Ensuring that traffic moves smoothly and minimizing the waiting time in intersections requires automated vehicle detection techniques for controlling the traffic light automatically, which are still challenging problems. In this paper, we propose an intelligent traffic pattern collection and analysis model, named TPCAM, based on traffic cameras to help in smooth vehicular movement on junctions and set to reduce the traffic congestion. Our traffic detection and pattern analysis model aims at detecting and calculating the traffic flux of vehicles and pedestrians at intersections in real-time. Our system can utilize one camera to capture all the traffic flows in one intersection instead of multiple cameras, which will reduce the infrastructure requirement and potential for easy deployment. We propose a new deep learning model based on YOLOv2 and adapt the model for the traffic detection scenarios. To reduce the network burdens and eliminate the deployment of network backbone at the intersections, we propose to process the traffic video data at the network edge without transmitting the big data back to the cloud. To improve the processing frame rate at the edge, we further propose deep object tracking algorithm leveraging adaptive multi-modal models and make it robust to object occlusions and varying lighting conditions. Based on the deep learning based detection and tracking, we can achieve pseudo-30FPS via adaptive key frame selection. 

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4. Real-Time Video Anonymization in Smart City Intersections

   A. Angus, Z. Duan ( January 2023 , in Proc. IEEE MASS’22 (invited), 2022.)

   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 deeplearning models based on YOLOv4 for detection of privacysensitive 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. Index Terms—Smart City, Sensors, Video Surveillance, Privacy Protection, Object Detection, Deep Learning, TensorRT. 

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5. Real-Time Video Anonymization in Smart City Intersections

   A. Angus, Z. Duan ( January 2022 , in Proc. IEEE MASS’22 (invited), 2022 (invited))

   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 deeplearning models based on YOLOv4 for detection of privacysensitive 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. Index Terms—Smart City, Sensors, Video Surveillance, Privacy Protection, Object Detection, Deep Learning, TensorRT. 

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