Year: 2022
Type: conference-paper
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.
Institution: Columbia University
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Awards: CAREER: Adaptive Algorithms for Combinatorial Optimization in Stochastic Networks, CPS: Medium: Hybrid Twins for Urban Transportation: From Intersections to Citywide Management, RINGS: Enabling Wireless Edge-cloud Services via Autonomous Resource Allocation and Robust Physical Layer Technologies, G4578656615, PAWR Platform COSMOS: Cloud Enhanced Open Software Defined Mobile Wireless Testbed for City-Scale Deployment, G5903004777, NSF-BSF: CNS Core: Small: Improving Wireless Networks Robustness via Weather-Sensitive Predictive Management, IRNC: Testbed: COSMOS Interconnecting Continents (COSMIC)
OpenAlex ID: W4283011377