TY - GEN
T1 - The 2019 AI city challenge
AU - Naphade, Milind
AU - Tang, Zheng
AU - Chang, Ming Ching
AU - Anastasiu, David C.
AU - Sharma, Anuj
AU - Chellappa, Rama
AU - Wang, Shuo
AU - Chakraborty, Pranamesh
AU - Huang, Tingting
AU - Hwang, Jenq Neng
AU - Lyu, Siwei
N1 - Publisher Copyright:
© 2019 IEEE Computer Society. All rights reserved.
PY - 2019/6
Y1 - 2019/6
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85097914342
M3 - Conference contribution
AN - SCOPUS:85097914342
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 452
EP - 460
BT - Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
PB - IEEE Computer Society
T2 - 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
Y2 - 16 June 2019 through 20 June 2019
ER -