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The 2019 AI city challenge

  • Milind Naphade
  • , Zheng Tang
  • , Ming Ching Chang
  • , David C. Anastasiu
  • , Anuj Sharma
  • , Rama Chellappa
  • , Shuo Wang
  • , Pranamesh Chakraborty
  • , Tingting Huang
  • , Jenq Neng Hwang
  • , Siwei Lyu
  • NVIDIA
  • University of Washington
  • SUNY Albany
  • San Jose State University
  • Iowa State University
  • University of Maryland, College Park

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

72 Scopus citations

Abstract

The AI City Challenge has been created to accelerate intelligent video analysis that helps make cities smarter and safer. With millions of traffic video cameras acting as sensors around the world, there is a significant opportunity for real-time and batch analysis of these videos to provide actionable insights. These insights will benefit a wide variety of agencies, from traffic control to public safety. The 2019 AI City Challenge is the third annual edition in the AI City Challenge series with significant growing attention and participation. AI City Challenge 2019 enabled 334 academic and industrial research teams from 44 countries to solve real-world problems using real city-scale traffic camera video data. The Challenge was launched with three tracks. Track 1 addressed city-scale multi-camera vehicle tracking, Track 2 addressed city-scale vehicle re-identification, and Track 3 addressed traffic anomaly detection. Each track was chosen in consultation with departments of transportation focusing on problems of greatest public value. With the largest available dataset for such tasks, and ground truth for each track, the 2019 AI City Challenge received 129 submissions from 96 individuals teams (there were 22, 84, 23 team submissions from Tracks 1, 2, and 3 respectively). Participation in this challenge has grown five-fold this year as tasks have become more relevant to traffic optimization and challenging to the computer vision community. Results observed strongly underline the value AI brings to city-scale video analysis for traffic optimization.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
PublisherIEEE Computer Society
Pages452-460
Number of pages9
ISBN (Electronic)9781728125060
StatePublished - Jun 2019
Event32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019 - Long Beach, United States
Duration: Jun 16 2019Jun 20 2019

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume2019-June
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
Country/TerritoryUnited States
CityLong Beach
Period06/16/1906/20/19

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