The 2019 AI City Challenge
Published in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition - 3rd AI City Challenge Workshop, 2019
Recommended citation: Milind Naphade, Zheng Tang, Ming-Ching Chang, David C Anastasiu, Anuj Sharma, Rama Chellappa, Shuo Wang, Pranamesh Chakraborty, Tingting Huang, Jenq-Neng Hwang and Siwei Lyu. "The 2019 AI City Challenge". Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2019). pp. 452-460. 2019. http://openaccess.thecvf.com/content_CVPRW_2019/html/AI_City/Naphade_The_2019_AI_City_Challenge_CVPRW_2019_paper.html
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.
BibTeX
@inproceedings{Naphade19AICity19,
author = {Milind Naphade and Zheng Tang and Ming-Ching Chang and David C. Anastasiu and Anuj Sharma and Rama Chellappa and Shuo Wang and Pranamesh Chakraborty and Tingting Huang and Jenq-Neng Hwang and Siwei Lyu},
title = {The 2019 {AI} {C}ity {C}hallenge},
booktitle = {Proc. CVPR Workshops},
pages = {452–460},
address = {Long Beach, CA, USA},
year = {2019}
}