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1. 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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2. Deterring Deepfake Attacks with an Electrical Network Frequency Fingerprints Approach

   https://doi.org/10.3390/fi14050125  

   Nagothu, Deeraj; Xu, Ronghua; Chen, Yu; Blasch, Erik; Aved, Alexander (May 2022, Future Internet)

   With the fast development of Fifth-/Sixth-Generation (5G/6G) communications and the Internet of Video Things (IoVT), a broad range of mega-scale data applications emerge (e.g., all-weather all-time video). These network-based applications highly depend on reliable, secure, and real-time audio and/or video streams (AVSs), which consequently become a target for attackers. While modern Artificial Intelligence (AI) technology is integrated with many multimedia applications to help enhance its applications, the development of General Adversarial Networks (GANs) also leads to deepfake attacks that enable manipulation of audio or video streams to mimic any targeted person. Deepfake attacks are highly disturbing and can mislead the public, raising further challenges in policy, technology, social, and legal aspects. Instead of engaging in an endless AI arms race “fighting fire with fire”, where new Deep Learning (DL) algorithms keep making fake AVS more realistic, this paper proposes a novel approach that tackles the challenging problem of detecting deepfaked AVS data leveraging Electrical Network Frequency (ENF) signals embedded in the AVS data as a fingerprint. Under low Signal-to-Noise Ratio (SNR) conditions, Short-Time Fourier Transform (STFT) and Multiple Signal Classification (MUSIC) spectrum estimation techniques are investigated to detect the Instantaneous Frequency (IF) of interest. For reliable authentication, we enhanced the ENF signal embedded through an artificial power source in a noisy environment using the spectral combination technique and a Robust Filtering Algorithm (RFA). The proposed signal estimation workflow was deployed on a continuous audio/video input for resilience against frame manipulation attacks. A Singular Spectrum Analysis (SSA) approach was selected to minimize the false positive rate of signal correlations. Extensive experimental analysis for a reliable ENF edge-based estimation in deepfaked multimedia recordings is provided to facilitate the need for distinguishing artificially altered media content. 

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3. Distributed Edge Cloud R-CNN for Real Time Object Detection

   Herrera, Joshua; Demir, Mevlut; Prevost, John; Rad, Paul (July 2018, World Automation Congress proceedings)

   Cloud computing infrastructures have become the de-facto platform for data driven machine learning applications. However, these centralized models of computing are unqualified for dispersed high volume real-time edge data intensive applications such as real time object detection, where video streams may be captured at multiple geographical locations. While many recent advancements in object detection have been made using Convolutional Neural Networks but these performance improvements only focus on a single contiguous object detection model. In this paper, we propose a distributed Edge-Cloud R-CNN by splitting the model into components and dynamically distributing these components in the cloud for optimal performance for real time object detection. As a proof of concept, we evaluate the performance of the proposed system on a distributed computing platform encompasses cloud servers and edge embedded devices for real-time object detection on video streams. 

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4. 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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5. 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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