2019 AI CITY CHALLENGE
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.
Notice
The datasets for the 2019 AI City Challenge, i.e., CityFlow (for MTMC vehicle tracking and re-identification) and the Iowa DOT Traffic Dataset (for traffic anomaly detection), can be accessed now. The evaluation server is open again for submission of test results. Please follow the Datasets Access Instructions to submit the completed Datasets Request Form to us.
IMPORTANT DATES
-
Challenge kick off: Jan 3 -
Data sets shared with participants: Jan 10 -
Challenge track submissions due: May 10 (09:00 AM, Pacific Daylight Time)
Evaluation submission is closed and rankings are finalized. -
Workshop papers (camera-ready) due: May 11 (09:00 AM, Pacific Daylight Time)
Here is the link for paper submission: https://cmt3.research.microsoft.com/AICCVPRW2019/
Since our review is not double-blind, papers should be submitted in final/camera-ready form. -
Final decisions to authors: May 15
All authors are notified in CMT. There are about 24 hours to prepare for the final version of accepted papers. -
Final papers due: May 16 (11:59 PM, Pacific Daylight Time)
All camera-ready papers should be uploaded to CMT to be published by CVPR 2019. -
Open source on GitHub (training code + testing code + additional annotation) due: June 1 (11:59 PM, Pacific Daylight Time)
All the competitors/candidates for awards MUST release their code for validation before decision of awardees. -
Presentation of papers and announcement of awards: June 16
WORKSHOP PROGRAM
AI City Challenge CVPR Workshop 2019
- Sunday, June 16, 2019
- Long Beach, CA
- Long Beach Convention Center - Room 101B
09:00 – 09:30
Workshop Kickoff and Opening Comments
09:30 – 10:10
Two Oral Presentations (20 minutes each)
10:10 – 10:30
Coffee Break
10:30 – 12:10
Five Oral Presentations (20 minutes each)
(5) Vehicle Re-identification with Learned Representation and Spatial Verification and Abnormality Detection with Multi-adaptive Vehicle Detectors for Traffic Video Analysis
(6) A Comparative Study of Faster R-CNN Models for Anomaly Detection in 2019 AI City Challenge
(7) Attention Driven Vehicle Re-identification and Unsupervised Anomaly Detection for Traffic Understanding
12:10 – 13:40
Lunch
13:40 – 15:20
Five Oral Presentations (20 minutes each)
(9) Multi-view Vehicle Re-identification Using Temporal Attention Model and Metadata Re-ranking
(10) Partition and Reunion: A Two-branch Neural Network for Vehicle Re-identification
(11) Multi-camera Vehicle Tracking with Powerful Visual Features and Spatial-temporal Cue
(12) Vehicle Re-identifiation and Multi-camera Tracking in Challenging City-scale Environment
15:20 – 15:50
Coffee Break
15:50 – 16:30
Two Oral Presentations (20 minutes each)
(14) A Locality Aware City-scale Multi-Camera Vehicle Tracking System
16:30 – 17:00
Announcement of Challenge Winners and Awards Ceremony
17:00 – 18:00
Poster/Demo Session
18:00
Adjourn
ORGANIZING COMMITTEE
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
CITATIONS
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 paper is available here. The reference to our paper is posted below.
@InProceedings{Tang_2019_CVPR,
author = {Tang, Zheng and Naphade, Milind and Liu, Ming-Yu and Yang, Xiaodong and Birchfield, Stan and Wang, Shuo and Kumar, Ratnesh and Anastasiu, David and Hwang, Jenq-Neng},
title = {CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}
The Challenge summary paper (The 2019 AI City Challenge) has been published in the proceedings of 2019 CVPRW. Please refer to these two papers when you continue your work on this dataset.
@InProceedings{Naphade19AIC19,
author = {Naphade, Milind and Tang, Zheng and Chang, Ming-Ching and Anastasiu, David C. and Sharma, Anuj and Chellappa, Rama and Wang, Shuo and Chakraborty, Pranamesh and Huang, Tingting and Hwang, Jenq-Neng and Lyu, Siwei},
title = {The 2019 AI City Challenge},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019},
pages = {452–460}
}