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Jun 20, 2021 2:29 PM
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Overview

Data for this challenge comes from multiple traffic cameras from a city in the United States as well as from state highways in Iowa. Specifically, we have time-synchronized video feeds from several traffic cameras spanning major travel arteries of the city. Most of these feeds are high resolution 1080p feeds at 10 frames per second. The vantage point of these cameras is for traffic and transportation purposes and the data will be redacted in terms of faces and license plates to address data privacy issues. Moreover, we have built a synthetic vehicle data set consisting of over 1,300 distinct vehicles and over 140,000 images. These synthetic images will form an augmented training set to be used along with the real-world traffic data set for Tracks 2 and 3.

  • Urban Intersection and Highway Data for Vehicle Counting – About 9 hours of videos captured from 20 different vantage points (including intersection single approaches, full intersections, highway segments and city streets) covering various lighting and weather conditions (including dawn, rain, and snow). Videos are 960p or better, and most have been captured at 10 frames per second.
  • Urban Intersection and Highway Data for Multi-Camera Vehicle Tracking – Nearly 3 hours of synchronized videos synchronously captured from multiple vantage points at various urban intersections and along highways. Videos are 960p or better, and most have been captured at 10 frames per second.
  • Synthetic Vehicle Data for Vehicle Re-ID – Over 190,000 images of over 1,300 distinct vehicles. These synthetic images will form an augmented training set to be used along with the real-world data for vehicle re-identification and multi-camera vehicle tracking tasks.
  • Iowa State University Data – More than 25 hours of video data captured on highways in Iowa.
  • Metadata about the collected videos, including GPS locations of cameras, camera calibration information and other derived data from videos.

Citation

Please cite the following papers accordingly if you choose to work with our datasets or refer to the previous challenge results:

1.Vehicle MTMC dataset – CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification

@InProceedings{Tang_2019_CVPR, author = {Zheng Tang and Milind Naphade and Ming-Yu Liu and Xiaodong Yang and Stan Birchfield and Shuo Wang and Ratnesh Kumar and David Anastasiu and Jenq-Neng Hwang},

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} }

2.Synthetic 3D vehicle dataset – Simulating content consistent vehicle datasets with attribute descent

@InProceedings{Yao19VehicleX, author={Yue Yao and Liang Zheng and Xiaodong Yang and Milind Naphade and Tom Gedeon},

title = {Simulating content consistent vehicle datasets with attribute descent}, howpublished = {arXiv:1912.08855}, year = {2019} }

3.2020 challenge summary paper – The 4th AI City Challenge

@InProceedings{Naphade20AIC20, author = {Milind Naphade and Shuo Wang and David C. Anastasiu and Zheng Tang and Ming-Ching Chang and Xiaodong Yang and Liang Zheng and Anuj Sharma and Rama Chellappa and Pranamesh Chakraborty}.

title = {The 4th AI City Challenge},

booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2020}

}

🎉Many thanks to Graviti Open Datasets for contributing the dataset
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Updated on2021-01-20 03:43:08
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