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The NVIDIA AI City Challenge

  • Milind Naphade
  • , David C. Anastasiu
  • , Anuj Sharma
  • , Vamsi Jagrlamudi
  • , Hyeran Jeon
  • , Kaikai Liu
  • , Ming Ching Chang
  • , Siwei Lyu
  • , Zeyu Gao
  • NVIDIA
  • San Jose State University
  • Iowa State University
  • SUNY Albany
  • Iowa State University
  • SUNY Albany

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

40 Scopus citations

Abstract

Web image analysis has witnessed an AI renaissance. The ILSVRC benchmark has been instrumental in providing a corpus and standardized evaluation. The NVIDIA AI City Challenge is envisioned to provide similar impetus to the analysis of image and video data that helps make cities smarter and safer. In its first year, this Challenge has focused on traffic video data. While millions of traffic video cameras around the world capture data, albeit low-quality, very little automated analysis and value creation results. Lack of labeled data, and trained models that can be deployed at the edge of the city fabric, ensure that most traffic video data goes through little or no automated analysis. Real-Time and batch analysis of this data can provide vital breakthroughs in real-Time traffic management as well as pedestrian safety. The NVIDIA AI City Challenge brought together 29 teams from universities in 4 continents to collaboratively annotate a 125 hour data set and then compete on detection, localization and classification tasks as well as traffic and safety application analytics tasks. The result is the largest high quality annotated data set, a set of models trained using NVIDIA AI City Edge to Cloud platform and ready to be deployed at the edge solving traffic and safety problems for cities worldwide.

Original languageEnglish
Title of host publication2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538604342
DOIs
StatePublished - Jun 26 2018
Event2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017 - San Francisco, United States
Duration: Apr 4 2017Apr 8 2017

Publication series

Name2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017 - Conference Proceedings

Conference

Conference2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017
Country/TerritoryUnited States
CitySan Francisco
Period04/4/1704/8/17

Keywords

  • AI
  • Deep Learning
  • GPU
  • Intersection over Union
  • cloud computing
  • edge computing
  • mean Average Precision
  • pedestrian safety
  • traffic flow
  • video analysis

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