Skip to main content
Presentation
The 2018 NVIDIA AI City Challenge
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
  • Milind Naphade, NVIDIA Corporation
  • Ming-Ching Chang, University at Albany, State University of New York
  • Anuj Sharma, Iowa State University
  • David C. Anastasiu, San Jose State University
  • Vamsi Jagarlamudi, Iowa State University
  • Pranamesh Chakraborty, Iowa State University
  • Tingting Huang, Iowa State University
  • Shuo Wang, NVIDIA Corporation
  • Ming-Yu Liu, NVIDIA Corporation
  • Rama Chellappa, University at Maryland
  • Jenq-Neng Hwang, University of Washington
  • Siwei Lyu, University at Albany, State University of New York
Document Type
Conference Proceeding
Conference
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Publication Version
Accepted Manuscript
Link to Published Version
https://doi.org/10.1109/CVPRW.2018.00015
Publication Date
1-1-2018
DOI
10.1109/CVPRW.2018.00015
Conference Title
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Conference Date
June 18-22, 2018
Geolocation
(40.7607793, -111.89104739999999)
Abstract

The NVIDIA 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 second edition of the NVIDIA AI City Challenge, being organized as a CVPR workshop, provided a forum to more than 70 academic and industrial research teams to compete and solve real-world problems using traffic camera video data. The Challenge was launched with three tracks - speed estimation, anomaly detection, and vehicle re-identification. Each track was chosen in consultation with traffic and public safety officials based on the value of potential solutions. With the largest available dataset for such tasks, and ground truth for each track, the Challenge enabled 22 teams to evaluate their solutions. Given how complex these tasks are, the results are encouraging and reflect increased value addition year over year for the Challenge.

Comments

This is a manuscript of a proceeding published as Naphade, Milind, Ming-Ching Chang, Anuj Sharma, David C. Anastasiu, Vamsi Jagarlamudi, Pranamesh Chakraborty, Tingting Huang et al. "The 2018 NVIDIA AI City Challenge." In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), (2018): 53-60. DOI: 10.1109/CVPRW.2018.00015. Posted with permission.

Rights
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Copyright Owner
IEEE
Language
en
File Format
application/pdf
Citation Information
Milind Naphade, Ming-Ching Chang, Anuj Sharma, David C. Anastasiu, et al.. "The 2018 NVIDIA AI City Challenge" Salt Lake City, UT2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2018) p. 53 - 60
Available at: http://works.bepress.com/anuj_sharma1/79/