Immense opportunity exists to make transportation systems smarter, based on sensor data from traffic, signaling systems, infrastructure, and transit.  Unfortunately, progress has been limited for several reasons — among them, poor data quality, missing data labels, and the lack of high-quality models that can convert the data into actionable insights There is also a need for platforms that can handle analysis from the edge to the cloud, which will accelerate the development and deployment of these models.

We are organizing the AI City Challenge Workshop at CVPR 2019 to help address these challenges by encouraging research and development into techniques that rely less on supervised approaches and more on transfer learning, unsupervised and semi-supervised approaches that go beyond bounding boxes.  It will focus on Intelligent Transportation System (ITS) problems, such as:

  • City-scale multi-camera vehicle tracking
  • City-scale multi-camera vehicle re-identification
  • Traffic anomaly detection – Leveraging unsupervised learning to detect anomalies such as lane violation, illegal U-turns, wrong-direction driving, etc.

We solicit original contributions in these and related areas where computer vision and specifically deep learning have shown promise in achieving large-scale practical deployment that will help make cities smarter.

To accelerate the research and development of techniques that rely less on supervised approaches and more on transfer learning, self-supervised and semi-supervised learning we are organizing this Challenge.

IMPORTANT NOTICE: Evaluation system is now open. See ‘Challenge’->’Evaluation System’ for instructions or click here.



Estimated number of orals, posters, and invited talks, with rough program outline

  • Oral Presentations: 12
  • Invited Keynotes: 2

Program Outline

09:00 – 09:15

Workshop Kickoff and Opening Comments

09:15 – 09:45

First Keynote Speech

09:45 – 10:00

Coffee Break

10:00 – 12:00

Six Oral Presentations (20 minutes each)

12:00 – 13:00


13:00 – 13:30

Second Keynote Speech

13:30 – 15:30

Six Oral presentations (20 minutes each)

15:30 – 16:00


16:00 – 16:30

Announcement of Challenge Winners and Awards Ceremony

16:30 – 17:30



Milind Naphade

NVIDIA Corporation

Rama Chellappa

University of Maryland, College Park

David Anastasiu

San Jose State University

Anuj Sharma

Iowa State University

Ming-Ching Chang

University at Albany – SUNY

Ming-Yu Liu

NVIDIA Research

Xiaodong Yang

NVIDIA Research

Siwei Lyu

University at Albany – SUNY

Jenq-Neng Hwang

University of Washington, Seattle


Our paper based on the benchmarks of Track 1 and Track 2 of the 2019 AI City Challenge has been accepted for oral presentation at the CVPR 2019 main conference. The teams participating in the 2019 AI City Challenge will be among the first researchers working on this benchmark, and we believe your work will create a significant impact in cross-camera vehicle tracking and re-identification. Please note that the official name of our benchmark for Track 1 is “CityFlow,” and the benchmark of Track 2 is a subset of that, named “CityFlow-ReID.” The camera-ready paper has been put on arXiv (https://arxiv.org/abs/1903.09254). The reference to our paper is posted below. 


    author = {Zheng Tang and Milind Naphade and Ming-Yu Liu and Xiaodong Yang and Stan Birchfield and Shuo Wang and Ratnesh Kumar and David C. Anastasiu and Jenq-Neng Hwang},

    booktitle = {CVPR 2019: IEEE Conference on Computer Vision and Pattern Recognition},

    title = {CityFlow: A city-scale benchmark for multi-target multi-camera vehicle tracking and re-identification},

    year = {2019}